Multi-robot distributed robust safety consensus control method system

Abstract

The disclosure provides a multi-robot distributed robust safety consensus control method and system, and relates to the technical field of multi-robot distributed robust safety consensus control. The multi-robot distributed robust safety consensus control method comprises the following steps: determining a position error variable and a speed error variable of any one robot in a multi-robot at a preset time; determining a distributed sliding mode surface corresponding to the preset time by using the position error variable and the speed error variable of the any one robot at the preset time, and position relative state variables and speed relative state variables corresponding to the multi-robot; and realizing distributed robust safety consensus control of the multi-robot based on the distributed sliding mode surface. The embodiment of the disclosure can realize multi-robot distributed robust safety consensus control.

Description

Technical Field

[0001] This disclosure relates to the field of distributed robust safety consensus control technology for multi-robot systems, and in particular to a distributed robust safety consensus control method and system for multi-robot systems. Background Technology

[0002] With the rapid development of multi-robot system control, distributed cooperative control has become a key method for multi-robot coordination due to its high efficiency. However, in real-world environments, robot swarms often need to perform collaborative tasks in unknown environments, making safety their top priority. Therefore, designing a control scheme that can achieve distributed, safe, and cooperative operation is a challenge. Currently, various algorithms focus on multi-robot obstacle avoidance, such as the artificial potential field method, which uses repulsive forces near obstacles to avoid collisions. However, these methods may cause sudden velocity changes, jeopardizing motion safety. To address this issue, model predictive control (MPC) is used to precisely handle obstacle avoidance constraints, optimizing robot trajectories within a finite future range by solving numerical optimization problems in real time. However, MPC-based methods require strict initial feasibility conditions and incur a heavy computational burden through online optimization at each step.

[0003] As a mainstream tool for synthetic safety controllers, the Control Barrier Function (CBF) has attracted widespread attention due to its computational efficiency. It uses continuously differentiable functions with specific properties to embed safety constraints into the QP (Quickness-Based Programming) framework. By solving online optimization problems with input constraints, it constructs a stable safety-critical controller, ensuring that the system state always remains within a safety-invariant set, effectively guaranteeing the real-time performance of the control algorithm. However, the relative order of traditional control barrier functions is typically 1, while the actual constraints of multi-robot systems often have higher relative orders. Furthermore, this method heavily relies on the accurate model of the system. Therefore, there is an urgent need to propose a technical solution for a multi-robot distributed robust safety consensus control method and system based on high-order control barrier functions. Summary of the Invention

[0004] This disclosure proposes a multi-robot distributed robust security consensus control method and corresponding technical solution.

[0005] According to one aspect of this disclosure, a distributed robust safety consensus control method for multiple robots is provided, comprising: determining the position error variable and velocity error variable of any one of the multiple robots at a preset time; using the position error variable and velocity error variable of the any one robot at the preset time, and the position relative state variable and velocity relative state variable of the multiple robots, determining the distributed sliding surface corresponding to the preset time; and realizing the distributed robust safety consensus control of the multiple robots based on the distributed sliding surface.

[0006] Preferably, determining the position error variable of any one of the multiple robots at a preset time includes: using the position of any one of the multiple robots at the preset time and a reference position to determine the position error variable.

[0007] Preferably, determining the position error variable using the position of any one of the multiple robots at a preset time and a reference position includes: calculating the difference between the position of any one of the multiple robots at the preset time and the reference position of any one of the multiple robots at the preset time, and determining the position error variable.

[0008] Preferably, before determining the position error variable using the position and reference position of any one of the multiple robots at a preset time, the method includes: determining the position of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference position of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

[0009] Preferably, determining the speed error variable of any one of the multiple robots at a preset time includes: using the speed of any one of the multiple robots at the preset time and a reference speed to determine the speed error variable.

[0010] Preferably, determining the speed error variable using the speed of any one of the multiple robots at a preset time and a reference speed includes: calculating the difference between the speed of any one of the multiple robots at a preset time and the reference speed of any one of the multiple robots at a preset time, and determining the speed error variable.

[0011] Preferably, before determining the speed error variable using the speed and reference speed of any one of the multiple robots at a preset time, the process includes: determining the speed of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference speed of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

[0012] Preferably, determining the distributed sliding surface corresponding to the preset time using the position error variable and velocity error variable of any one robot at the preset time, and the position relative state variable and velocity relative state variable of the multiple robots, includes: determining the relative state variable of the multiple robots using the position relative state variable and the velocity relative state variable of the multiple robots; and determining the distributed sliding surface corresponding to the preset time based on the relative state variable of the multiple robots, the position error variable of any one robot at the preset time, and the velocity error variable of any one robot at the preset time.

[0013] Preferably, determining the relative state variables of the multiple robots using the relative position state variables and the relative velocity state variables of the multiple robots includes: determining the joint weighted relative state variables corresponding to the relative position state variables and the relative velocity state variables as the relative state variables of the multiple robots.

[0014] Preferably, determining the joint weighted relative state variable corresponding to the position relative state variable and the velocity relative state variable as the relative state variable corresponding to the multiple robots includes: using a first weight parameter and a second weight parameter to perform weight processing on the position relative state variable and the velocity relative state variable corresponding to the multiple robots respectively, to obtain the corresponding weighted position relative state variable and weighted velocity relative state variable; and determining the relative state variable corresponding to the multiple robots by using the joint weighted relative state variable corresponding to the weighted position relative state variable and the weighted velocity relative state variable.

[0015] Preferably, determining the relative position state variables corresponding to the multiple robots includes: determining the relative position state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix.

[0016] Preferably, determining the relative velocity state variables corresponding to the multiple robots includes: determining the relative velocity state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacency velocities corresponding to the adjacency matrix.

[0017] Preferably, determining the relative position state variables of the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix includes: symbolizing the adjacent positions using a sign function to obtain adjacent symbol positions; exponentializing the adjacent symbol positions using a second exponential parameter to obtain adjacent symbol exponential positions; calculating the product of each adjacent symbol exponential position in the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple first products; and summing the multiple first products to obtain the relative position state variables of the multiple robots.

[0018] Preferably, determining the relative velocity state variable of the multiple robots based on the adjacency matrix and the adjacency velocity corresponding to the adjacency matrix includes: symbolizing the adjacency velocities using a symbolic function to obtain adjacency symbolic velocities; exponentializing the adjacency symbolic velocities using a third exponential parameter to obtain adjacency symbolic exponential velocities; calculating the product of each adjacency symbolic exponential velocity in the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple second products; and summing the multiple second products to obtain the relative velocity state variable of the multiple robots.

[0019] Preferably, determining the distributed sliding surface corresponding to the preset time based on the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time includes: combining the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time to obtain a joint state error variable, thereby determining the distributed sliding surface corresponding to the preset time.

[0020] Preferably, the step of combining the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time, includes: using a sign function to symbolize the position error variable corresponding to any one robot at the preset time to obtain a position error sign variable; and combining the relative state variables corresponding to the multiple robots, the error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time.

[0021] Preferably, the step of combining the relative state variables corresponding to the multiple robots, the position error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time, includes: exponentializing and / or weighting the position error sign variable using a first exponential parameter and / or a third weight parameter to obtain a position error sign-processed variable; and combining the relative state variables corresponding to the multiple robots, the position error sign-processed variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time.

[0022] Preferably, the step of realizing distributed robust safety consensus control of the multiple robots based on the distributed sliding surface includes: determining the nominal robust consensus controller corresponding to the preset time according to the distributed sliding surface equivalent controller and the finite-time reaching law expression corresponding to the distributed sliding surface; and determining the target control input of any robot at the preset time based on the safety constraints of the higher-order control barrier function safety set and the nominal robust consensus controller, so as to realize the distributed robust safety consensus control of the multiple robots.

[0023] Preferably, determining the equivalent controller of the distributed sliding surface corresponding to the distributed sliding surface includes: obtaining the first derivative of the distributed sliding surface to obtain the derivative of the distributed sliding surface; setting the derivative of the distributed sliding surface to a preset value to determine the corresponding equivalent controller of the distributed sliding surface.

[0024] Preferably, the step of determining the target control input of any robot at a preset time based on the safety constraints of the higher-order control barrier function safety set and the nominal robust consensus controller to achieve distributed robust safety consensus control of the multiple robots includes: under the safety constraints of the higher-order control barrier function safety set, calculating the position error variable and velocity error variable corresponding to the control input variable of the nominal robust consensus controller corresponding to the optimization variable to be solved, minimizing the distributed optimal coordinated control objective function, and determining the target control input corresponding to the optimization variable to be solved.

[0025] Preferably, determining the safety set of higher-order control obstacle functions includes: determining the continuously differentiable functions and the safety set of continuously differentiable functions associated with any robot and multiple obstacles at a preset time; performing successive differentiation on the continuously differentiable functions to obtain higher-order control obstacle functions; and determining the safety set of higher-order control obstacle functions based on the higher-order control obstacle functions and the safety set of continuously differentiable functions.

[0026] Preferably, determining the security constraints of the higher-order control barrier function safety set includes: determining the security constraints of the higher-order control barrier function safety set based on the higher-order control barrier function and the higher-order control barrier function safety set.

[0027] According to one aspect of this disclosure, a multi-robot distributed robust safety consensus control system is provided, comprising: a first determining unit, configured to determine the position error variable and velocity error variable of any one of the multiple robots at a preset time; a second determining unit, configured to determine the distributed sliding surface corresponding to the preset time using the position error variable and velocity error variable of the arbitrary robot at the preset time, and the position relative state variable and velocity relative state variable of the multiple robots; and a control unit, configured to implement distributed robust safety consensus control of the multiple robots based on the distributed sliding surface.

[0028] According to one aspect of this disclosure, a multi-robot distributed robust security consensus control system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned multi-robot distributed robust security consensus control method; or, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by the processor, implement the aforementioned multi-robot distributed robust security consensus control method to generate the bit stream; or, comprising: a computer program product configured with a computer program / instructions, wherein the computer program / instructions, when executed by the processor, implement the aforementioned multi-robot distributed robust security consensus control method.

[0029] In this disclosure, a multi-robot distributed robust safety consensus control method and system are proposed to address the technical problems of insufficient safety control capabilities in existing high-order robot control systems and interference environments. By applying the proposed distributed sliding surface, safety constraints of the safety set of high-order control barrier functions, and nominal robust consensus controller, the goal of achieving distributed optimal safety consensus in an uncertain environment for the multi-robot system is ensured.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0031] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0033] Figure 1 A flowchart illustrating a multi-robot distributed robust safety consensus control method according to an embodiment of the present disclosure is shown. Figure 2 This diagram illustrates a global security consensus diagram for a multi-robot system according to an embodiment of the present disclosure. Figure 3 This diagram illustrates the minimum distance between a multi-robot system and obstacles according to an embodiment of the present disclosure. Figure 4 This illustrates a multi-robot consensus diagram according to an embodiment of the present disclosure; Figure 5 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment; Figure 6 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0034] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0035] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0036] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0037] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0038] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0039] In addition, this disclosure also provides a multi-robot distributed robust safety consensus control system or device, electronic equipment, computer-readable storage medium, and program, all of which can be used to implement any of the multi-robot distributed robust safety consensus control methods provided in this disclosure. The corresponding technical solutions and descriptions are described in the relevant section on multi-robot distributed robust safety consensus control methods and will not be repeated here.

[0040] Figure 1 A flowchart illustrating a multi-robot distributed robust safety consensus control method according to embodiments of the present disclosure is shown, such as... Figure 1 As shown, the multi-robot distributed robust safety consensus control method includes: Step S101: Determining the position error variable and velocity error variable of any one robot in the multi-robot system at a preset time; Step S102: Using the position error variable and velocity error variable of the robot at the preset time, and the position relative state variable and velocity relative state variable of the multi-robot system, determining the distributed sliding surface corresponding to the preset time; Step S103: Based on the distributed sliding surface, realizing the distributed robust safety consensus control of the multi-robot system. By proposing technical solutions such as the distributed sliding surface, the safety constraints of the safety set of higher-order control barrier functions, and the nominal robust consensus controller, this method addresses the technical problems of insufficient safety control capabilities in existing high-relative-order robot control systems and under interference environments, thereby ensuring that the multi-robot system achieves the goal of distributed optimal safety consensus in uncertain environments.

[0041] Step S101: Determine the position error variable and velocity error variable of any one of the multiple robots at a preset time.

[0042] In the embodiments of this disclosure, determining the position error variable of any one of the multiple robots at a preset time includes: using the position of any one of the multiple robots at the preset time and a reference position to determine the position error variable.

[0043] In the embodiments of this disclosure, determining the position error variable using the position of any one of the multiple robots at a preset time and a reference position includes: calculating the difference between the position of any one of the multiple robots at the preset time and the reference position of any one of the multiple robots at the preset time, and determining the position error variable.

[0044] In the embodiments of this disclosure, before determining the position error variable using the position and reference position of any one of the multiple robots at a preset time, the method includes: determining the position of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference position of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

[0045] In the embodiments of this disclosure, determining the speed error variable corresponding to any one of the multiple robots at a preset time includes: using the speed of any one of the multiple robots at the preset time and a reference speed to determine the speed error variable.

[0046] In the embodiments of this disclosure, determining the speed error variable using the speed of any one of the multiple robots at a preset time and a reference speed includes: calculating the difference between the speed of any one of the multiple robots at the preset time and the reference speed of any one of the multiple robots at the preset time, and determining the speed error variable.

[0047] In embodiments of this disclosure, before determining the speed error variable using the speed and reference speed of any one of the multiple robots at a preset time, the method includes: determining the speed of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference speed of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

[0048] In embodiments and other possible embodiments of this disclosure, the graph theory foundation is as follows: Define a fixed undirected graph G=(V,E,A), which consists of a set of N vertices V={1,...,N} and an undirected edge set E=V×V. An edge (i, j) in the undirected edge set E indicates that the i-th vertex and the j-th vertex can communicate bidirectionally, and the ij-edge from the i-th vertex to the j-th vertex and the ji-edge from the j-th vertex to the i-th vertex represent the same edge. The adjacency matrix A={ } is an N×N matrix, where edge (i, j) E, that is, when the i-th vertex and the j-th vertex are connected, then =1, otherwise =0. This fixed undirected graph G has no self-loops, meaning that no vertex is connected to itself. Therefore, all elements on the diagonal of the adjacency matrix A are zero. =0). For a specific vertex i (the i-th vertex), all vertices directly connected to it (the j-th vertex) constitute the neighborhood set of the i-th vertex. ,Right now The Laplace matrix of a fixed undirected graph G Defined as Where D is a diagonal matrix, called the degree matrix, and its i-th diagonal element is... (The set of neighbors corresponding to the i-th vertex) The determinant of the matrix is ​​the degree of the i-th vertex.

[0049] In embodiments and other possible embodiments of this disclosure, establishing a multi-robot dynamics model based on a second-order integrator includes: considering a class of robots with... N The dynamic equations of a multi-robot system with one robot are modeled as follows: (1) in, , and Let represent the position, velocity, control input, and external disturbance of the i-th robot at time t, respectively, and let n represent the dimension of the multi-robot system. and These represent the positions of the i-th robot at time t. The velocity of the i-th robot at time t The first derivative.

[0050] In embodiments and other possible embodiments of this disclosure, a distributed optimal trajectory coordinator based on saddle point dynamics is established, including: defining the performance function of a multi-robot system and establishing the distributed optimal trajectory coordinator.

[0051] In embodiments and other possible embodiments of this disclosure, a performance function for a multi-robot system is defined. For ease of writing, a performance function for a multi-robot system is defined. (col represents a column vector), for the dynamic equation (1) corresponding to a multi-robot system, we define Define the global performance function of the multi-robot system as follows: The distributed optimal coordination control problem of a multi-robot system can then be reformulated as the distributed optimal coordination control objective function: (2) in, Represented as robot i The local cost function for the position at time t, , Denotes an n-order identity matrix; where, Let G be the Laplacian matrix of a fixed undirected graph; where, N Each robot is configured to correspond to a fixed undirected graph G and a set V = {1, ..., N}. That is to say... NThe i-th and j-th robots in the set of ... } is an N×N matrix, where edge (i, j) E, that is, when the i-th robot and the j-th robot are connected, then =1, otherwise =0.

[0052] In embodiments of this disclosure and other possible embodiments, a distributed optimal trajectory coordinator is established.

[0053] (3a) (3b) in, Let i represent the set of neighbors of the i-th robot. This represents the reference position of the i-th robot at time t. r (Reference to the description) Let represent the reference position of the j-th robot at time t. The relative position reference state variable between the i-th robot and the j-th robot is represented. For the i-th robot, the auxiliary variable (state variable) is... The first derivative is the sum of the relative position reference state variables between the i-th robot and the j-th robot. Let i be the relative state variable between the i-th robot and the j-th robot. For the i-th robot at time i t State variables, z j For the j-th robot at time... t (state variables). Represents robots i Local cost function of the position at time t The gradient vector. Based on the distributed optimal trajectory coordinator (3a)-(3b), the reference position of the i-th robot at time t can be obtained. .

[0054] Lemma 1: Definition 1) If yes The saddle point, then The solution to (2); 2) when If the solution is (2), then there exists a solution that satisfies Auxiliary variables z , making yes A saddle point.

[0055] To verify that the distributed optimal trajectory coordinator (3a)-(3b) can converge to the optimal solution of the distributed optimal coordinated control problem (2), we define This is the optimal solution to the distributed optimal coordination control problem (2); where, , This represents the optimal position for the i-th robot. The optimal position for the global system is defined for N robots, ensuring that all robots are in the same state.

[0056] Constructing Lyapunov functions V 1 Defined as: (4) Taking the first derivative of equation (4), based on Lemma 1, and considering equations (3a)-(3b), we can obtain (5) Therefore, when When it is a saddle point, Established, the reference trajectories generated by the coordinator (3a)-(3b) It can converge asymptotically to the saddle point. The reference positions of N robots at time t Auxiliary variables (state variables) of N robots at time t. ; Let represent the first derivative of the reference position of N robots at time t; Let represent the first derivative of the auxiliary variables (state variables) of N robots at time t; For N robots, the global system's optimal auxiliary variables (optimal state variables) are: This represents the L2 norm.

[0057] Step S102: Using the position error variable and velocity error variable of any one robot at a preset time, and the position relative state variable and velocity relative state variable of the multiple robots, determine the distributed sliding surface corresponding to the preset time.

[0058] In the embodiments of this disclosure, determining the distributed sliding surface corresponding to the preset time using the position error variable and velocity error variable of any one robot at a preset time, and the position relative state variable and velocity relative state variable of the multiple robots, includes: determining the relative state variable of the multiple robots using the position relative state variable and the velocity relative state variable of the multiple robots; and determining the distributed sliding surface corresponding to the preset time based on the relative state variable of the multiple robots, the position error variable of any one robot at the preset time, and the velocity error variable of any one robot at the preset time.

[0059] In embodiments of this disclosure and other possible embodiments, the relative positional state variables corresponding to the multiple robots are utilized. and the velocity relative state variables corresponding to the multiple robots Determine the relative state variables corresponding to the multiple robots. Based on the relative state variables corresponding to the multiple robots The position error variable corresponding to any one of the robots at a preset time. and the velocity error variable corresponding to any one of the robots at a preset time. Determine the distributed sliding surface corresponding to the preset time. .

[0060] In the embodiments of this disclosure, determining the relative state variables corresponding to the multiple robots using the position relative state variables and the velocity relative state variables corresponding to the multiple robots includes: determining the joint weight relative state variables corresponding to the position relative state variables and the velocity relative state variables as the relative state variables corresponding to the multiple robots.

[0061] In embodiments of this disclosure and other possible embodiments, the position relative to the state variable is... and the velocity relative state variable The corresponding joint weights relative to the state variables ( ), which are determined as the relative state variables corresponding to the multiple robots. ( ).

[0062] In embodiments of this disclosure, determining the joint weighted relative state variable corresponding to the position relative state variable and the velocity relative state variable as the relative state variable corresponding to the multiple robots includes: using a first weight parameter and a second weight parameter to perform weight processing on the position relative state variable and the velocity relative state variable corresponding to the multiple robots, respectively, to obtain corresponding weighted position relative state variables and weighted velocity relative state variables; and determining the relative state variable corresponding to the multiple robots by using the joint weighted relative state variable corresponding to the weighted position relative state variable and the weighted velocity relative state variable.

[0063] In embodiments and other possible embodiments of this disclosure, a first weighting parameter is utilized. and the second weighting parameter The relative position state variables and the relative velocity state variables corresponding to the multiple robots are weighted respectively to obtain the corresponding weighted relative position state variables. and weighted velocity relative to state variables The weight position is relative to the state variable. and the weighted velocity relative state variable The corresponding joint weights relative to the state variables Determine the relative state variables corresponding to the multiple robots. .

[0064] In embodiments of this disclosure, determining the relative position state variables corresponding to the multiple robots includes: determining the relative position state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix.

[0065] In embodiments and other possible embodiments of this disclosure, determining the positional relative state variables corresponding to the multiple robots includes: based on the adjacency matrix A=[ a ij and the adjacent positions corresponding to the adjacency matrix. Determine the relative position state variables of the multiple robots. or .

[0066] In embodiments of this disclosure, determining the relative velocity state variables corresponding to the multiple robots includes: determining the relative velocity state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent velocities corresponding to the adjacency matrix.

[0067] In embodiments and other possible embodiments of this disclosure, according to the adjacency matrix A corresponding to the multiple robots, A=[ a ijand the adjacency speed corresponding to the adjacency matrix. Determine the relative velocity state variables of the multiple robots. or .

[0068] In embodiments of this disclosure, determining the relative position state variables of the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix includes: symbolizing the adjacent positions using a sign function to obtain adjacent symbol positions; exponentializing the adjacent symbol positions using a second exponential parameter to obtain adjacent symbol exponential positions; calculating the product of each adjacent symbol exponential position in the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple first products; and summing the multiple first products to obtain the relative position state variables of the multiple robots.

[0069] In the embodiments and other possible embodiments of this disclosure, symbolic functions are utilized respectively. For the adjacent positions Perform symbolization to obtain the adjacent symbol positions. Using the second exponential parameter For the position of the adjacent symbol Perform indexing to obtain the index positions of adjacent symbols. ; Calculate the index position of each adjacency symbol in the multi-robot system respectively. Adjacency relationships in the adjacency matrix A The product of these first products yields multiple first products. ; for the plurality of first products Summation is performed to obtain the position-relative state variables corresponding to the multiple robots. .

[0070] In embodiments of this disclosure, determining the relative velocity state variable corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacency velocity corresponding to the adjacency matrix includes: symbolizing the adjacency velocities using a symbolic function to obtain adjacency symbolic velocities; exponentializing the adjacency symbolic velocities using a third exponential parameter to obtain adjacency symbolic exponential velocities; calculating the product of each adjacency symbolic exponential velocity in the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple second products; and summing the multiple second products to obtain the relative velocity state variable corresponding to the multiple robots.

[0071] In the embodiments and other possible embodiments of this disclosure, symbolic functions are utilized respectively. For the adjacent speed Perform symbolization to obtain the adjacent symbol speed. Using the third exponential parameter The speed of the adjacent symbols Exponentialization is performed to obtain the exponential speed of adjacent symbols. ; Calculate the exponential velocity of each adjacent symbol in the multi-robot system respectively. Adjacency relationships in the adjacency matrix A The product of the first product yields multiple second products. Summing the multiple second products yields the velocity-relative state variables corresponding to the multiple robots. .

[0072] In the embodiments of this disclosure, determining the distributed sliding surface corresponding to the preset time based on the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time includes: combining the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time to obtain a joint state error variable, thereby determining the distributed sliding surface corresponding to the preset time.

[0073] In embodiments of this disclosure, the step of combining the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time, includes: using a sign function to symbolize the position error variable corresponding to any one robot at the preset time to obtain a position error sign variable; and combining the relative state variables corresponding to the multiple robots, the error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time.

[0074] In embodiments and other possible embodiments of this disclosure, symbolic functions are utilized. For any one of the robots at a preset time, the position error variable Symbolization is performed to obtain the symbolic variable of the position error. ; for the relative state variables corresponding to the multiple robots The error sign variable corresponding to any one of the robots at a preset time. and the velocity error variable corresponding to any one of the robots at a preset time. By combining the variables, we obtain the joint state error variables. To determine the distributed sliding surface corresponding to the preset time. .

[0075] In embodiments of this disclosure, the step of combining the relative state variables corresponding to the multiple robots, the position error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time, includes: exponentializing and / or weighting the position error sign variable using a first exponential parameter and / or a third weight parameter to obtain a position error sign-processed variable; and combining the relative state variables corresponding to the multiple robots, the position error sign-processed variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, in order to determine the distributed sliding surface corresponding to the preset time.

[0076] In embodiments and other possible embodiments of this disclosure, the first exponential parameter is utilized. and / or third weighting parameter For the position error sign variable After indexing and / or weighting, the sign-processed variable of the position error is obtained. or or ; for the relative state variables corresponding to the multiple robots The position error sign processing variable corresponding to any one of the robots at a preset time. or or Combined with the velocity error variable corresponding to any one of the robots at a preset time. The joint state error variables are obtained. or or To determine the distributed sliding surface corresponding to the preset time. or or .

[0077] In the embodiments and other possible embodiments disclosed herein, a nominal robust consensus controller is designed based on a multi-robot dynamics model and a distributed optimal trajectory coordinator (optimal trajectory coordinator), combined with a super-helical sliding mode control method / algorithm.

[0078] In the embodiments and other possible embodiments disclosed herein, a multi-robot dynamics model is used to determine the position and velocity of the i-th robot at time t; a distributed optimal trajectory coordinator (optimal trajectory coordinator) is used to determine the reference position of the i-th robot at time t and the velocity corresponding to the reference position of the i-th robot at time t.

[0079] In the embodiments and other possible embodiments of this disclosure, definitions are used for ease of writing. , Let the sign function be defined. Define the position error variable and velocity error variable of the i-th robot at time t. , ( This represents the position of the i-th robot at time t. This represents the reference position of the i-th robot at time t. This represents the velocity of the i-th robot at time t. (Representing the velocity corresponding to the reference position of the i-th robot at time t), establish the distributed sliding surface of the i-th robot.

[0080] (6a) (6b) Wherein, the distributed sliding surface of the i-th robot First design parameters Second design parameters Third design parameters satisfy First design parameters Second design parameters Third design parameters satisfy . This represents the weight relative state variable / relative state variable (weight relative state quantity / relative state) corresponding to N robots. or This represents the relative positional state of N robots to be weighted (the relative positional state variable to be weighted). or This represents the relative velocity states (relative velocity state variables) of N robots to be weighted. This represents the adjacency relationship between the i-th robot and the j-th robot in the adjacency matrix A; This represents the relative position (adjacent position) between the i-th robot and the j-th robot. This represents the relative velocity (adjacency velocity) between the i-th robot and the j-th robot.

[0081] Step S103: Based on the distributed sliding surface, realize the distributed robust safety consensus control of the multi-robot system.

[0082] In the embodiments of this disclosure, the step of implementing distributed robust safety consensus control of the multiple robots based on the distributed sliding surface includes: determining the nominal robust consensus controller corresponding to the preset time according to the distributed sliding surface equivalent controller and the finite-time convergence law expression corresponding to the distributed sliding surface; and determining the target control input of any robot at the preset time based on the safety constraints of the higher-order control barrier function safety set and the nominal robust consensus controller, so as to realize the distributed robust safety consensus control of the multiple robots.

[0083] In embodiments of this disclosure, determining the equivalent controller of the distributed sliding surface corresponding to the distributed sliding surface includes: obtaining the first derivative of the distributed sliding surface to obtain the derivative of the distributed sliding surface; setting the derivative of the distributed sliding surface to a preset value to determine the corresponding equivalent controller of the distributed sliding surface.

[0084] In the embodiments of this disclosure, the step of determining the target control input of any robot at a preset time based on the safety constraints of the higher-order control barrier function safety set and the nominal robust consensus controller to achieve distributed robust safety consensus control of the multiple robots includes: under the safety constraints of the higher-order control barrier function safety set, calculating the position error variable and velocity error variable corresponding to the control input variable of the nominal robust consensus controller corresponding to the optimization variable to be solved, minimizing the distributed optimal coordinated control objective function, and determining the target control input corresponding to the optimization variable to be solved.

[0085] In embodiments of this disclosure, determining the safety set of higher-order control obstacle functions includes: determining the continuously differentiable functions and the safety set of continuously differentiable functions associated with any robot and multiple obstacles at a preset time; performing successive differentiation on the continuously differentiable functions to obtain higher-order control obstacle functions; and determining the safety set of higher-order control obstacle functions based on the higher-order control obstacle functions and the safety set of continuously differentiable functions.

[0086] In embodiments of this disclosure, determining the security constraints of the higher-order control barrier function security set includes: determining the security constraints of the higher-order control barrier function security set based on the higher-order control barrier function and the higher-order control barrier function security set.

[0087] In the embodiments and other possible embodiments of this disclosure, the finite-time reaching law corresponding to the i-th robot based on the superhelical algorithm (superhelical sliding mode control algorithm) is... The design is as follows: (7) in, This represents the first control gain and the second control gain of the i-th robot. It is the integral variable.

[0088] Based on equivalent control theory, the distributed sliding surface of the i-th robot is obtained. The first derivative is used to obtain the distributed sliding surface derivative. Setting the distributed sliding surface derivative corresponding to the i-th robot's distributed sliding surface to 0, we obtain the distributed sliding surface equivalent controller. And based on the finite-time convergence law The corresponding finite-time reaching law expression, Obtain the nominal consensus controller (nominal robust consensus controller) for the i-th robot. It can be designed as follows: (8) Find the distributed sliding surface of the i-th robot. The first derivative of the distributed sliding surface is obtained; substituting the nominal consensus controller (nominal robust consensus controller) (8) into the distributed sliding surface derivative, we can further write it as: (9a) (9b) To analyze the control performance of the nominal consensus controller (nominal robust consensus controller) (8) on the multi-robot dynamics model (1), the Lyapunov function of the i-th robot is constructed as follows: Among them, Lyapunov parameters , , Let represent the positive definite symmetric matrix of the i-th robot. Taking the derivative, we obtain the Lyapunov derivative function; substituting equations (9a)-(9b) into the Lyapunov derivative function, we obtain the Lyapunov derivative function corresponding to the distributed sliding surface derivative: (10) in, satisfy ,and For Hurwitz, .

[0089] Next, equation (10) can be further simplified to (11) in, External disturbance corresponding to the i-th robot The known upper bound, and These are the minimum and maximum eigenvalues ​​of the corresponding matrices, respectively. , , and These are the positive definite symmetric matrices of the i-th robot. The first and second elements of the first row. Therefore, the trajectory of the multi-robot dynamics model (1) is driven to the distributed sliding surface under the nominal consensus controller (nominal robust consensus controller) (8). .

[0090] In embodiments and other possible embodiments of this disclosure, the provision of safety constraints for a multi-robot system through a higher-order control obstacle function includes: establishing a distance-based obstacle detection function (higher-order control obstacle function), defining a continuously differentiable function safety set, and introducing... The system classifies functions and defines higher-order control barrier functions by successive differentiation, and derives robust safety constraints based on the safety set of higher-order control barrier functions.

[0091] In embodiments of this disclosure and other possible embodiments, a distance-based obstacle detection function (higher-order obstacle control function) is established.

[0092] First, for N A simplified geometric model is established for obstacles in the robot's working environment. To reduce computational complexity, planar circles are used to approximate irregular obstacles. N Collision detection between a robot and an obstacle is transformed into determining whether the minimum distance is positive; a higher-order obstacle control function is defined, where m=2nd order, a continuously differentiable function related to the i-th robot and the q-th obstacle: (12).

[0093] in, This represents the relative position vector between the position of the i-th robot and the centroid of the q-th obstacle. , Represents the set of obstacles. Let represent the distance (2-norm) between the i-th robot and the q-th obstacle. The preset safe distance between the i-th robot and the obstacle. At that time, the i-th robot remains safe.

[0094] In embodiments of this disclosure and other possible embodiments, a safe set of continuously differentiable functions is defined.

[0095] To construct the safety of continuously differentiable functions under the premise of enforced set invariance, it is necessary to define the safety set (safety set) corresponding to the safety; define the safety set (safety set of continuously differentiable functions) for the i-th robot. for: (13) For any initial time robot location The initial solution satisfies , That is, for any time t, the robot's position always remains within the safe set, then the set is forward invariant, i.e., the multi-robot dynamics model (1) for the safe set It is safe.

[0096] In embodiments and other possible embodiments of this disclosure, the following are introduced: Class functions are defined, and higher-order control barrier functions are defined by successive differentiation.

[0097] If a Lipschitz continuous function It is strictly increasing and satisfies Then the function is said to belong to Class function; Define helper function By analyzing the function By taking the derivatives sequentially, we can obtain the higher-order control barrier function (HOCBF): (14) Without loss of generality, definition The class function is a linear function, that is , ,in, and This represents the control gain of the first linear function and the control gain of the second linear function.

[0098] Equation (14) corresponds to the sequence of higher-order control obstacle function safety sets for the i-th robot. for (15) In embodiments and other possible embodiments of this disclosure, robust safety constraints based on a safety set of higher-order control barrier functions are derived. Based on equation (14), we can obtain... (16) in, .

[0099] when hour, External disturbances for the i-th robot Given the upper bound, the robust safety constraint set based on higher-order control barrier functions (safety constraints of the higher-order control barrier function safety set) is as follows: (17) Where U is the set of control inputs that satisfy the constraints. . The complete safety set of the i-th robot can be represented as: ,in, .

[0100] Satisfying set The control input of the i-th robot within This ensures the safety of the i-th robot. The forward direction remains unchanged, thus ensuring the safety of multiple robots.

[0101] In the embodiments and other possible embodiments of this disclosure, the combination of safety constraints and optimal control problem, and the solution of the target control input of the i-th robot corresponding to the optimal control input through quadratic programming (QP) are described. This includes: combining robust safety constraints (safety constraints) with the distributed optimal coordination control problem of multi-robot systems and solving quadratic programming to obtain real-time local optimal control inputs (target control inputs). ).

[0102] In embodiments and other possible embodiments of this disclosure, robust safety constraints (safety constraints) are combined with the distributed optimal coordination control problem of multi-robot systems: To minimize the optimization variables to be solved for the i-th robot Input to the nominal consensus controller (nominal robust consensus controller) of the i-th robot The deviation, combined with robust safety constraints (safety constraints) (17), the optimal control problem based on HOCBF-QP can be expressed as a distributed optimal coordinated control objective function and its corresponding safety constraints: (18) in, The target control input for the i-th robot satisfies the safety constraints. Let be the optimization variable to be solved for the i-th robot. The control inputs for the nominal robust consensus controller of the i-th robot (including position error variables and velocity error variables) , When the system satisfies the safety constraints, the solution to the optimization problem is: This indicates that the control input of the i-th robot is dominated by the nominal robust consensus controller; when the state of the i-th robot leaves the safe set of continuously differentiable functions... At this time, mandatory safety constraints are added, sacrificing control performance to meet safety requirements, until multiple robots move away from the obstacle; In embodiments of this disclosure and other possible embodiments, a quadratic programming problem is solved to obtain the real-time locally optimal control input (target control input). ): The distributed optimal coordination control problem of this multi-robot system is solved at each time step, with the time interval... Divided into a series of unequal time intervals: {[ t 0, t 0+Δ t 1),[ t 0+Δ t 1, t 0+Δ t 1+Δ t 2),…}(19) Where, Δ t 1>0; in each interval Keeping the state constant at the beginning of its interval and assuming constant control, the optimization problem (18) is expressed as a QP sequence; specifically, in Solve QP: The standard form of QP is: (20) For a symmetric positive definite matrix H and a constant vector F, neglecting the constant term, we get H = 2. F=-2 ; Transform the constraint inequality in (18) into In the form of (twenty one) (twenty two) Substituting H, F, A, and B into the QP solver and solving using Matlab, the resulting solution vector represents the optimal control inputs for robot i in the x and y directions for the current time interval. .

[0103] To demonstrate the effectiveness of the proposed method, this invention provides a simulation experiment with four mobile robot systems, and the corresponding Laplacian matrix is... The initial position and velocity of each robot system are set as follows: , , The obstacle position can be set as follows: , , , Assume the time-varying perturbation is taken as... , , , .

[0104] To drive the robots from different initial positions to a common position, the optimal local cost function of the i-th robot is... Represented as .

[0105] In the simulation experiment, each robot used the same control parameters in the nominal controller. The sliding surface parameters are designed as follows: , , The superspiral control gain is designed as follows: In the i-th robot safety controller In the design, the safe distance between the robot and the obstacle is set to... , .

[0106] Figure 2 This diagram illustrates a global security consensus diagram for a multi-robot system according to an embodiment of the present disclosure. Figure 3 This diagram illustrates the minimum distance between a multi-robot system and obstacles according to an embodiment of the present disclosure. Figure 4 A multi-robot consensus graph is shown according to an embodiment of this disclosure. For example... Figures 2-4 The corresponding control effect diagram of the robot implementing robust safety consensus is shown in the figure. Figure 2 It can be seen that under the proposed robust security consensus control strategy, interference is considered. d i All robots can move to the target location along the optimal trajectory, indicating that the distributed optimal safety consensus of the multi-robot system has been successfully achieved and has good robustness. Figure 3 As can be seen, the robot and obstacles always maintain a safe distance, further demonstrating that the proposed robust safety consensus control strategy has excellent obstacle avoidance capabilities. Figure 4 This indicates that all robot positions eventually converge to a consensus point. In summary, for multi-robot systems operating in environments with unknown obstacles, the proposed controller can ensure optimal safety consensus among robots under safety constraints.

[0107] In summary, this disclosure includes the following steps: establishing a multi-robot dynamics model based on a second-order integrator; defining a global performance function for the multi-robot system and establishing a distributed optimal trajectory coordinator based on saddle point dynamics to generate a basic reference trajectory; designing a nominal robust consensus controller based on the multi-robot dynamics model and the optimal trajectory coordinator, combined with a super-helical sliding mode control method, and verifying the system stability using a Lyapunov function; providing safety constraints for the multi-robot system through a higher-order control obstacle function, establishing a distance-based obstacle detection function, defining a safety set, introducing class functions, defining auxiliary functions through successive differentiation, and deriving the multi-robot obstacle avoidance safety constraints; combining safety constraints with the optimal control problem, solving for the optimal control input through quadratic programming; and performing system safety analysis to prove the forward invariance of the system safety set.

[0108] This invention addresses multi-robot systems and, in the context of unknown obstacle environments, designs a distributed safety consensus method for multi-robot systems based on a high-order control obstacle function, combining a nominal robust consensus controller to ensure that the multi-robot system achieves safety consensus while satisfying safety constraints.

[0109] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a hierarchical distributed method that decouples the planning layer and the safety control layer. The upper layer uses a distributed optimal trajectory coordinator to generate the optimal reference trajectory, while the lower layer achieves safety consensus based on a robust higher-order control obstacle function, exhibiting good scalability. Compared with traditional PID, adaptive, and other control methods, the proposed distributed nominal sliding mode consensus controller (nominal robust consensus controller) has faster consensus speed and stronger robustness. The multi-robot distributed robust safety consensus control method based on a higher-order control obstacle function proposed in this invention ensures that the multi-robot system always meets safety requirements in unknown obstacle environments, effectively improving the safety of the multi-robot system and guaranteeing the real-time performance of the control algorithm.

[0110] In summary, the technical concept of this invention is as follows: For multi-robot systems operating in environments with unknown obstacles, a high-order robust obstacle control function suitable for high-relative-order systems is employed. The specific method includes: establishing a multi-robot dynamics model; establishing a distributed optimal trajectory coordinator based on saddle point dynamics to generate a reference trajectory; designing a nominal robust consensus controller using a super-spiral sliding mode control method; establishing a distance-based obstacle detection function to provide safety constraints for the multi-robot system through the high-order obstacle control function; and combining safety constraints with the optimal control problem, solving for the optimal control input through quadratic programming to ensure the forward invariance of the system's safety set. This method ensures that the multi-robot system achieves optimal safety consensus while satisfying safety constraints, exhibiting good robustness and safety, and is suitable for robot systems with disturbances, thus possessing high engineering application value.

[0111] The multi-robot distributed robust security consensus control method can be executed by a multi-robot distributed robust security consensus control device or system. For example, the multi-robot distributed robust security consensus control method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, this multi-robot distributed robust security consensus control method can be implemented by a processor calling computer-readable instructions stored in memory.

[0112] Those skilled in the art will understand that in the above-described multi-robot distributed robust safety consensus control method in specific implementations, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0113] This disclosure also provides a multi-robot distributed robust safety consensus control system, comprising: a first determining unit, configured to determine the position error variable and velocity error variable of any one of the multiple robots at a preset time; a second determining unit, configured to determine the distributed sliding surface corresponding to the preset time using the position error variable and velocity error variable of the arbitrary robot at the preset time, and the position relative state variable and velocity relative state variable of the multiple robots; and a control unit, configured to implement distributed robust safety consensus control of the multiple robots based on the distributed sliding surface.

[0114] This disclosure also provides a multi-robot distributed robust security consensus control system, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned multi-robot distributed robust security consensus control method; or, a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by the processor, implement the aforementioned multi-robot distributed robust security consensus control method to generate the bit stream; or, a computer program product configured with a computer program / instructions, wherein the computer program / instructions, when executed by the processor, implement the aforementioned multi-robot distributed robust security consensus control method.

[0115] Figure 5This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0116] Reference Figure 5 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0117] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0118] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0119] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0120] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0121] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0122] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0123] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0124] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0125] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-described multi-robot distributed robust safety consensus control method.

[0126] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the above-described multi-robot distributed robust security consensus control method.

[0127] Figure 6 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 6 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0128] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0129] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above-described multi-robot distributed robust security consensus control method.

[0130] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0131] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0132] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0133] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0134] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0135] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0136] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0138] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A multi-robot distributed robust safety consensus control method, characterized in that, include: Determine the position error variable and velocity error variable of any one robot in a multi-robot group at a preset time. The distributed sliding surface corresponding to the preset time is determined by using the position error variable and velocity error variable of any one robot at a preset time, and the position relative state variable and velocity relative state variable of the multiple robots. Based on the distributed sliding surface, distributed robust safety consensus control of the multiple robots is realized.

2. The multi-robot distributed robust safety consensus control method according to claim 1, characterized in that, Determining the position error variable of any one robot in a multi-robot group at a preset time includes: determining the position error variable using the position of any one robot at the preset time and a reference position; and / or, The step of determining the position error variable using the position and reference position of any one robot in the multi-robot group at a preset time includes: calculating the difference between the position of any one robot in the multi-robot group at the preset time and the reference position of any one robot in the multi-robot group at the preset time, and determining the position error variable; and / or, Before determining the position error variable using the position and reference position of any one of the multiple robots at a preset time, the process includes: determining the position of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference position of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

3. The multi-robot distributed robust safety consensus control method according to any one of claims 1 or 2, characterized in that, Determining the speed error variable of any one robot in a multi-robot group at a preset time includes: determining the speed error variable using the speed of any one robot at the preset time and a reference speed; and / or, The step of determining the speed error variable using the speed of any one robot in the multi-robot group at a preset time and a reference speed includes: calculating the difference between the speed of any one robot in the multi-robot group at the preset time and the reference speed of any one robot in the multi-robot group at the preset time, and determining the speed error variable; and / or, Before determining the speed error variable using the speed and reference speed of any one of the multiple robots at a preset time, the process includes: determining the speed of any one robot at a preset time using the multi-robot dynamics model corresponding to the multiple robots; and determining the reference speed of any one robot at a preset time using the distributed optimal trajectory coordinator corresponding to the multiple robots.

4. The multi-robot distributed robust safety consensus control method according to any one of claims 1-3, characterized in that, The step of determining the distributed sliding surface at the preset time using the position error variable and velocity error variable of any one robot at a preset time, and the position relative state variable and velocity relative state variable of the multiple robots, includes: determining the relative state variable of the multiple robots using the position relative state variable and velocity relative state variable of the multiple robots; determining the distributed sliding surface at the preset time based on the relative state variable of the multiple robots, the position error variable of any one robot at the preset time, and the velocity error variable of any one robot at the preset time; and / or, The step of determining the relative state variables of the multiple robots using the relative position state variables and the relative velocity state variables of the multiple robots includes: determining the joint weighted relative state variables corresponding to the relative position state variables and the relative velocity state variables as the relative state variables of the multiple robots; and / or, The step of determining the joint weighted relative state variable corresponding to the position relative state variable and the velocity relative state variable as the relative state variable corresponding to the multiple robots includes: using a first weight parameter and a second weight parameter to perform weight processing on the position relative state variable and the velocity relative state variable corresponding to the multiple robots respectively, to obtain the corresponding weighted position relative state variable and weighted velocity relative state variable; and determining the relative state variable corresponding to the multiple robots by the joint weighted relative state variable corresponding to the weighted position relative state variable and the weighted velocity relative state variable.

5. The multi-robot distributed robust safety consensus control method according to claim 4, characterized in that, Determining the relative position state variables corresponding to the multiple robots includes: determining the relative position state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix; and / or, Determining the relative velocity state variables corresponding to the multiple robots includes: determining the relative velocity state variables corresponding to the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent velocities corresponding to the adjacency matrix; and / or, The step of determining the relative position state variables of the multiple robots based on the adjacency matrix corresponding to the multiple robots and the adjacent positions corresponding to the adjacency matrix includes: symbolizing the adjacent positions using a sign function to obtain adjacent symbol positions; exponentializing the adjacent symbol positions using a second exponential parameter to obtain adjacent symbol exponential positions; calculating the product of each adjacent symbol exponential position in the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple first products; summing the multiple first products to obtain the relative position state variables of the multiple robots; and / or, The step of determining the relative velocity state variable of the multiple robots based on the adjacency matrix and the adjacency velocity of the adjacency matrix includes: symbolizing the adjacency velocities using a symbolic function to obtain adjacency symbolic velocities; exponentializing the adjacency symbolic velocities using a third exponential parameter to obtain adjacency symbolic exponential velocities; calculating the product of each adjacency symbolic exponential velocity of the multiple robots with the adjacency relationship in the adjacency matrix to obtain multiple second products; and summing the multiple second products to obtain the relative velocity state variable of the multiple robots.

6. The multi-robot distributed robust safety consensus control method according to any one of claims 4 or 5, characterized in that, The step of determining the distributed sliding surface corresponding to the preset time based on the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time includes: jointly processing the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at the preset time, and the velocity error variable corresponding to any one robot at the preset time to obtain a joint state error variable, thereby determining the distributed sliding surface corresponding to the preset time; and / or, The step of combining the relative state variables corresponding to the multiple robots, the position error variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, and then determining the distributed sliding surface corresponding to the preset time, includes: symbolizing the position error variable corresponding to any one robot at the preset time using a sign function to obtain a position error sign variable; combining the relative state variables corresponding to the multiple robots, the error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, and then determining the distributed sliding surface corresponding to the preset time; and / or, The step of combining the relative state variables corresponding to the multiple robots, the position error sign variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, and then determining the distributed sliding surface corresponding to the preset time, includes: exponentializing and / or weighting the position error sign variable using a first exponential parameter and / or a third weight parameter to obtain a position error sign-processed variable; and combining the relative state variables corresponding to the multiple robots, the position error sign-processed variable corresponding to any one robot at a preset time, and the velocity error variable corresponding to any one robot at a preset time to obtain a joint state error variable, and then determining the distributed sliding surface corresponding to the preset time.

7. The multi-robot distributed robust safety consensus control method according to any one of claims 1-6, characterized in that, The method of implementing distributed robust safety consensus control of the multi-robot based on the distributed sliding surface includes: determining the nominal robust consensus controller corresponding to the preset time according to the distributed sliding surface equivalent controller and the finite-time approach law expression. Based on the safety constraints of the safety set of higher-order control barrier functions and the nominal robust consensus controller, the target control input of any robot at a preset time is determined to realize the distributed robust safety consensus control of the multiple robots.

8. The multi-robot distributed robust safety consensus control method according to claim 7, characterized in that, Determining the equivalent controller of the distributed sliding surface includes: calculating the first derivative of the distributed sliding surface to obtain the derivative of the distributed sliding surface; setting the derivative of the distributed sliding surface to a preset value to determine the corresponding equivalent controller of the distributed sliding surface; and / or, The method based on the safety constraints of a higher-order control barrier function safety set and a nominal robust consensus controller determines the target control input of any robot at a preset time to achieve distributed robust safety consensus control of the multiple robots. This includes: under the safety constraints of the higher-order control barrier function safety set, minimizing the distributed optimal coordinated control objective function by calculating the optimization variable to be solved and the control input variable of the nominal robust consensus controller; and / or, Determining the safety set of higher-order control obstacle functions includes: determining the continuously differentiable functions and the safety set of continuously differentiable functions associated with any robot and multiple obstacles at a preset time; performing successive differentiation on the continuously differentiable functions to obtain higher-order control obstacle functions; determining the safety set of higher-order control obstacle functions based on the higher-order control obstacle functions and the safety set of continuously differentiable functions; and / or, Determining the security constraints of the safety set of higher-order control barrier functions includes: determining the security constraints of the safety set of higher-order control barrier functions based on the higher-order control barrier functions and the safety set of higher-order control barrier functions.

9. A distributed robust and secure consensus control system for X-type multi-robot systems, characterized in that, include: The first determining unit is used to determine the position error variable and velocity error variable of any one of the multiple robots at a preset time. The second determining unit is used to determine the distributed sliding surface corresponding to the preset time by using the position error variable and velocity error variable of any one robot at the preset time, and the position relative state variable and velocity relative state variable of the multiple robots. The control unit is used to implement distributed robust safety consensus control of the multiple robots based on the distributed sliding surface.

10. A multi-robot distributed robust security consensus control system, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the multi-robot distributed robust safety consensus control method according to any one of claims 1-8; or, Includes: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the multi-robot distributed robust security consensus control method of any one of claims 1-8 to generate the bit stream; or, Includes: a computer program product, the computer program product being configured with a computer program / instruction, which, when executed by a processor, implements any one of the multi-robot distributed robust security consensus control methods described in 1-8.

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