A cross-domain motion planning method and system for an amphibious robot

Abstract

The application discloses a cross-domain motion planning method and system of an amphibious robot, and relates to the technical field of robot control. The method acquires operation constraints of the amphibious robot in a working space; a particle swarm optimization algorithm is used to plan a path according to the operation constraints; a dynamics model is constructed, a Gaussian pseudospectral method is used to construct a wing folding angle smooth switching trajectory with the minimum flight height drop in a mode switching process as a target; a nonlinear controller is set according to the dynamics model and a fuzzy algorithm, the nonlinear controller is used to control the amphibious robot according to an optimal land-air cross-domain path, and in addition, the wing folding angle is smoothly switched in a flight process according to the wing folding angle smooth switching trajectory. The application designs an adaptive fuzzy position controller to process nonlinear uncertainty in a dynamic model, carries out cross-domain motion planning based on an energy optimal path and smooth mode switching control, and enables the amphibious robot to realize stable motion and reliable operation in a cross-domain mode.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a cross-domain motion planning method and system for an amphibious robot. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Robots are widely used in industries such as power line inspection, logistics and transportation, and industrial production. However, traditional robots are limited to operating in a single environment and cannot meet the needs of complex scenarios. To address these challenges, amphibious robots, with their excellent mobility, adaptability, and flexibility, are used in applications such as disaster relief, environmental monitoring, military operations, and agriculture and industry. Among them, land-and-air amphibious robots have attracted much attention due to their wide range of applications, low energy consumption, and high operational efficiency.

[0004] Typically, amphibious robots are equipped with multiple rotors for aerial flight and multiple wheels for ground movement, enabling cross-domain operation. However, due to the fusion of ground and aerial modalities, amphibious robots face various challenges, such as complex nonlinear models, uncertain dynamic characteristics, system uncertainties, and instabilities caused by multimodal operation.

[0005] The aforementioned problems make motion planning and control of amphibious robots extremely difficult. Regarding the planning of amphibious robots, some researchers have proposed a hierarchical motion planner to generate hybrid ground-air paths and used the B-spline method to enhance safety, smoothness, and compliance with dynamic constraints. Other researchers have formulated optimal control problems and employed the trapezoidal coordination method for numerical solutions, obtaining the shortest time trajectory under spatially varying constraints. Still others have proposed a nonlinear model predictive control method considering complementary constraints of hybrid dynamics and evaluating uncertainty bounds along the nominal trajectory in a rolling range manner to ensure safety and robustness.

[0006] Furthermore, some progress has been made in the control of amphibious robots, with many efforts yielding commendable results. Existing research has designed a nonlinear controller with adaptive characteristics to counteract the effects of nonlinear uncertainties in the model and track aerial or ground trajectories. Other research has developed a nonlinear, multi-input, multi-output sliding mode controller based on the model and demonstrated its robustness to bounded parameter uncertainties using the Lyapunov direct method. Research has also analyzed the coupled nonlinear dynamics of the aircraft landing process, where the rotor acts as an active regulator to mitigate tire suspension vibration after landing. In addition, a model predictive control method with control assignment has been designed to improve aircraft landing stability.

[0007] However, despite the aforementioned efforts, amphibious robots still face the following challenges: 1) Due to their cross-domain mobility characteristics, motion planning for amphibious robots is a complex and constrained problem. 2) Due to dynamic system transitions, the mode switching process during cross-domain motion in amphibious robots is extremely unstable. However, few researchers have addressed this issue. 3) Amphibious robots exhibit highly nonlinear, strongly coupled, and uncertain dynamics.

[0008] Therefore, achieving accurate and stable tracking control for amphibious robots, which involve complex cross-domain motion planning, dynamic mode switching, and nonlinear strongly coupled dynamics during motion, is a major challenge. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the purpose of this invention is to provide a cross-domain motion planning method and system for amphibious robots. An adaptive fuzzy position controller is designed to handle nonlinear uncertainties in dynamic models. Cross-domain motion planning is performed based on energy-optimal path and smooth mode switching control, enabling amphibious robots to achieve stable motion and reliable operation across domains.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0011] The first aspect of this invention provides a cross-domain motion planning method for a land-air amphibious robot, comprising the following steps:

[0012] Determine the workspace of the amphibious robot based on mission requirements and obtain the operational constraints of the amphibious robot within the workspace.

[0013] The optimal land-air cross-domain path is obtained by using the particle swarm optimization algorithm to perform path planning based on operational constraints.

[0014] A dynamic model is constructed based on the dynamic parameters of the amphibious robot. The Gaussian pseudospectral method is used to construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process.

[0015] A nonlinear controller is set up based on the dynamic model and fuzzy algorithm. The nonlinear controller is used to control the amphibious robot according to the optimal land-air cross-domain path. In addition, the trajectory is smoothly switched during flight according to the wing folding angle.

[0016] Furthermore, the operational constraints include energy consumption cost constraints, collision cost constraints, and distance cost constraints. Specifically, energy consumption cost constraints are set based on the selection and switching of motion modes, collision cost constraints are set based on the positional relationship between the amphibious robot and obstacles in the workspace, and distance cost constraints are determined based on the segmented path between the amphibious robot and the destination.

[0017] Furthermore, the specific steps for path planning based on operational constraints using the particle swarm optimization algorithm are as follows:

[0018] Define the objective function for path planning;

[0019] Set the velocity and position update laws for each particle;

[0020] A novel inertial weight is designed based on the consideration of the problem of easily getting trapped in local optima;

[0021] The optimal land-air cross-domain path is obtained by iteratively solving the objective function based on the update law and the new inertial weights.

[0022] Furthermore, the dynamic model is a dynamic model of the aerial flight mode of the amphibious robot when the wing folding angle is zero.

[0023] Furthermore, using the Gaussian pseudospectral method to construct a smooth wing folding angle switching trajectory with the objective of minimizing the flight altitude drop during mode switching, the specific steps are as follows:

[0024] The dynamic model is transformed with the objective of minimizing the decrease in flight altitude during the mode switching process;

[0025] Set dynamic constraints;

[0026] A nonlinear programming function with algebraic constraints is constructed based on the transformation dynamic model and dynamic constraints.

[0027] Solving the nonlinear programming function yields the optimal state sequence for descent altitude.

[0028] Furthermore, dynamic constraints include folding angle constraints and flight descent constraints for amphibious robots.

[0029] Furthermore, the specific steps for setting up the nonlinear controller based on the dynamic model and fuzzy algorithm are as follows:

[0030] Construct the position control system function for the amphibious robot based on the dynamic model;

[0031] An adaptive fuzzy controller is constructed by introducing a fuzzy algorithm based on the nonlinear disturbance term in the position control system function.

[0032] A second aspect of the present invention provides a cross-domain motion planning system for a land-air amphibious robot, comprising:

[0033] The constraint determination module is configured to determine the workspace of the amphibious robot according to the task requirements and obtain the operational constraints of the amphibious robot within the workspace.

[0034] The path optimization module is configured to use the particle swarm optimization algorithm to perform path planning based on operational constraints and obtain the optimal land-air cross-domain path.

[0035] The wing switching module is configured to construct a dynamic model based on the dynamic parameters of the amphibious robot, and use the Gaussian pseudospectral method to construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process.

[0036] The motion control module is configured to set a nonlinear controller based on the dynamic model and fuzzy algorithm. The nonlinear controller controls the amphibious robot according to the optimal land-air cross-domain path. In addition, the trajectory is smoothly switched during flight according to the wing folding angle.

[0037] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps in the cross-domain motion planning method for a land-air amphibious robot as described in the first aspect of the present invention.

[0038] A fourth aspect of the present invention provides an apparatus including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the cross-domain motion planning method for a land-air amphibious robot as described in the first aspect of the present invention.

[0039] The above one or more technical solutions have the following beneficial effects:

[0040] This invention discloses a cross-domain motion planning method and system for amphibious robots, aiming to address the complex cross-domain motion planning, dynamic mode switching, and nonlinear strongly coupled dynamics issues encountered in current amphibious robot development. Based on an improved particle swarm optimization algorithm, this invention designs a novel path planning method to obtain an energy-optimal cross-domain path. Using the Gaussian pseudospectral method and an amphibious robot dynamics model, and with the objective of minimizing altitude loss during mode switching, a smooth wing folding angle switching trajectory is obtained. Based on the amphibious robot dynamics model, an adaptive fuzzy nonlinear controller is derived for the system. This invention features a simple structure, easily adjustable parameters, and convenient application. It can realize the planning and control of cross-domain motion for amphibious robots, improving their working efficiency and operational stability. It is applicable to the motion planning and control of variable-structure amphibious robots and has the advantages of convenient application and wide applicability.

[0041] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0042] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0043] Figure 1 This is a schematic diagram illustrating the working principle of the cross-domain motion planning method for the amphibious robot in Embodiment 1 of the present invention.

[0044] Figure 2 This is a diagram showing the planning results of the cross-domain path planning method in Embodiment 1 of the present invention;

[0045] Figure 3 This is a cost convergence diagram of the cross-domain path planning method in Embodiment 1 of the present invention;

[0046] Figure 4 This is a diagram showing the planning results of the smooth switching trajectory planning method in Embodiment 1 of the present invention;

[0047] Figure 5 This is a result diagram of the smooth trajectory switching method in Embodiment 1 of the present invention;

[0048] Figure 6 This is a result diagram of the adaptive fuzzy control method in Embodiment 1 of the present invention. Detailed Implementation

[0049] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] Example 1:

[0052] Embodiment 1 of the present invention provides a cross-domain motion planning method for a land-air amphibious robot, such as... Figure 1 As shown, it includes the following steps:

[0053] Step 1: Determine the workspace of the amphibious robot according to the task requirements and obtain the operational constraints of the amphibious robot within the workspace.

[0054] The operational constraints include energy consumption cost constraints, collision cost constraints, and distance cost constraints. Specifically, energy consumption cost constraints are set based on the selection and switching of motion modes, collision cost constraints are set based on the positional relationship between the amphibious robot and obstacles in the workspace, and distance cost constraints are determined based on the segmented path between the amphibious robot and the destination.

[0055] Specifically,

[0056] Based on mission requirements, the workspace of the amphibious robot is determined, and the energy-optimal cross-domain path can be represented by a sequence p as follows:

[0057] p = {p s p1, p2, ... p k-2 p k-1 p g} (1).

[0058] Where, p s p is the starting point g Let p1, ..., p be the target points. k-1 is the node of the planned path, and k is the number of segments of the planned path.

[0059] The goal of this invention is to enable amphibious robots to autonomously select ground or air movement modes based on environmental characteristics, thereby significantly improving accessibility and efficiency. To this end, an energy consumption cost function is first proposed, as shown below:

[0060]

[0061] Among them, J energy E represents the total energy cost of this path. s Let E be the energy cost of the s-th path segment, where 0 and 1 correspond to the ground and air motion modes, respectively. a E t The energy costs are calculated separately for air and ground motion. Air motion requires overcoming significantly more resistance than ground motion, and the motor speed during flight is considerably higher than that during ground travel. This represents the energy consumption coefficient.

[0062] Subsequently, to ensure the safety and stability of the amphibious robot operating in confined workspaces and complex obstacle environments, the collision cost function was artificially set as follows:

[0063]

[0064] Among them, Jcollision O is the total collision cost of the planned path. s Let O be the collision cost of the s-th path segment. t Costs incurred after a collision with an obstacle.

[0065] Finally, to ensure the shortest planned path length, the distance cost function is designed as follows:

[0066]

[0067] Among them, J distance L represents the total cost of the planned path distance. s Let be the length of the s-th path segment. During the planning process, it is difficult to avoid collisions, which inevitably incurs additional distance costs. However, in practice, the safe operation of the robot is the primary task. Therefore, when setting costs, the collision cost is artificially set to be much greater than the distance cost, so that the additional distance cost can be ignored.

[0068] Step 2: Use the particle swarm optimization algorithm to perform path planning based on operational constraints to obtain the optimal land-air cross-domain path.

[0069] Step 2.1: Define the objective function for path planning.

[0070] In one specific implementation, to obtain sequence p, the optimization objective is to minimize the total path cost J, defined as...

[0071]

[0072] Among them, J c (p) represents the cost associated with each constraint, W c This represents the weight coefficient of each constraint, NC is the number of constraints, and c represents the constraint label. In this embodiment, NC is 3, and c = energy, collision, or distance.

[0073] Step 2.2: Set the velocity and position update laws for each particle.

[0074] In one specific implementation, the energy-optimal cross-domain path is determined by cost functions (2)-(4). To solve the optimization problem (5), a cross-domain path planning method based on an improved particle swarm optimization algorithm is designed, wherein the velocity and position update laws of each particle are designed as follows:

[0075]

[0076] Where V and X are the particle's velocity and position, respectively, determined by vi in ​​the (t+1)th iteration. j and x ij Composition, P ib and Pgb Let ω be the position of the individual and the global extremum, respectively. ω is called the inertia weight and is a non-negative value. c1 and c2 are called learning factors and are non-negative constants. r1 and r2 are vectors composed of random values ​​between 0 and 1.

[0077] Step 2.3: Design a new type of inertial weight based on the consideration of the problem of easily getting trapped in local optima.

[0078] The improvement of the particle swarm optimization algorithm in this invention lies in the design of a more reasonable inertia weight, based on considerations of the tendency to get trapped in local optima. Compared with the traditional inertia weight, it can accelerate the solution process and improve the accuracy in solving the energy-optimal cross-domain path planning problem of this invention. The new inertia weight design is as follows:

[0079]

[0080] Where, ω M and ω m T represents the upper and lower bounds of the inertia weight, respectively. M λ represents the maximum number of iterations, and λ is a set constant.

[0081] Step 2.4: Iteratively solve the optimization objective function based on the update law and the new inertial weights to obtain the optimal land-air cross-domain path.

[0082] Specifically, based on the update law (6) and the new inertial weight (7), the energy-optimal cross-domain path sequence p can be obtained iteratively.

[0083] Step 3: Construct a dynamic model based on the dynamic parameters of the amphibious robot. Using the Gaussian pseudospectral method, construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process.

[0084] Step 3.1: Construct a dynamic model based on the dynamic parameters of the amphibious robot.

[0085] The wing folding angle is the angle between the wings and the fuselage of an amphibious robot. In its normal mode, this folding angle is zero, meaning the wings and fuselage are on the same horizontal line. During flight, when the wings fold downwards, it inevitably causes a change in the lift force acting on the robot, which can lead to instability or even a crash. Therefore, this embodiment proposes a trajectory planning method for smooth wing folding angle switching to achieve smooth mode switching in the air for the amphibious robot and ensure its operational safety.

[0086] This invention only relates to the control method for the robot's aerial mode; therefore, the mathematical model ignores the land mode. The dynamic model, specifically the dynamic model of the amphibious robot's aerial flight mode when the wing folding angle is zero, is expressed as follows:

[0087]

[0088] Among them, {x n y n , z n} represents the robot's position in the world coordinate system. and Representing velocity and acceleration, {φ, θ, ψ} and Let {I} be the robot's attitude angle, angular velocity, and angular acceleration. xx I yy I zz} represents the moment of inertia in the world coordinate system, τ1 represents the total thrust of the four propellers, and τ i i = 2, 3, 4 represent the torque in the fuselage coordinate system, w i Let i = 1, 2, ..., 6 represent external uncertain disturbances, and k fi Where i = 1, 2, 3 are the drag coefficients, m is the robot's mass, g is the acceleration due to gravity, and S is the speed of motion. Δ Represents sinΔ, C Δ Let cosΔ be the coordinate system. In the world coordinate system, the x-axis points east, the y-axis points north, and the z-axis points upward, with the robot's starting position as the origin.

[0089] Subsequently, the z-axis dynamic model of the amphibious robot under the action of the PID controller can be expressed as follows:

[0090]

[0091] Among them, z e For the tracking error, U0 = C ρ τ1-mg, k p k i k d Here are the parameters for the PID controller, and ρ is the wing folding angle. Furthermore, at the initial moment, U0 = 0.

[0092] Step 3.2: Using the Gaussian pseudospectral method, construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process.

[0093] Step 3.2.1: Transform the dynamic model with the goal of minimizing the decrease in flight altitude during the mode switching process.

[0094] The Gaussian pseudospectral method is a numerical method commonly used to solve optimal control and trajectory optimization problems in dynamic systems. This invention analyzes the altitude dynamics model of an amphibious robot, considering a series of safety constraints, and establishes an optimization problem. Using mathematical methods such as Gaussian integrals and Legendre polynomials, the optimal trajectory for wing folding angle variation is obtained, thereby reducing the impact of wing folding on flight altitude and ensuring smooth transitions.

[0095] In one specific implementation, to obtain a smooth switching trajectory with a folding angle that minimizes the descent height, the state variable ξ and control input u are first defined to facilitate the transformation of the altitude dynamics model:

[0096]

[0097] Among them, C ρ =cosρ, and C respectively ρ The first and second derivatives, For the tracking error z e The second derivative of .

[0098] Subsequently, the height dynamics model (10) can be transformed into the following:

[0099]

[0100] in,

[0101]

[0102] Step 3.2.2: Set dynamic constraints. These constraints include the folding angle constraints and flight descent constraints for the amphibious robot.

[0103] In one specific implementation, three sets of constraints will be set next to ensure stability during the switching process.

[0104] First, set the initial and final values ​​of the folding angles ρ, ξ, and u as follows:

[0105]

[0106] Then, the folding angle change rate and acceleration constraints are set as follows:

[0107]

[0108] in, and These are the upper bounds of the rate of change of the folding angle, the upper bound of the acceleration of the folding angle, the upper bound of the rate of change of the cosine value of the folding angle, and the upper bound of the acceleration of the cosine value of the folding angle.

[0109] In addition, the constraints on the robot's descent altitude, descent speed, and descent acceleration are set as follows:

[0110]

[0111] Among them, z emax , and It is the upper limit of descent altitude, descent speed, and descent acceleration.

[0112] Step 3.2.3: Construct a nonlinear programming function with algebraic constraints based on the transformation dynamic model and dynamic constraints.

[0113] In one specific implementation, a Legendre-Gaussian point sequence {t′0, t′1, t′2, ..., t′} is selected. K}, and introduce the Lagrange interpolation polynomial:

[0114]

[0115] Where, t′ i i = 0, ..., K is the Legendre-Gauss point. This represents the Grammar difference polynomial function.

[0116] The planned trajectory can then be approximated as:

[0117]

[0118] Where ξ(t′) and u(t′) represent the system state variables and input at t′, respectively, and the derivative of ξ(t′) with respect to time can be expressed as:

[0119]

[0120] in, It is the derivative of the Grand difference polynomial function.

[0121] Based on the above, this optimization problem can be transformed into a nonlinear programming problem with algebraic constraints, as follows:

[0122]

[0123] in,

[0124]

[0125] Step 3.2.4: Solve the nonlinear programming function to obtain the optimal state sequence for descent altitude.

[0126] In this embodiment, the optimal state sequence for descent altitude can be obtained by solving a quadratic programming method:

[0127] {ξ a min (t′0), ξ a min (t′1), ..., ξ a min (t′ K ), ξ a min (t′ K+1 )} (twenty two).

[0128] Where, ξ a min (t′ i ), i = 0, ..., K+1 are the optimal state sequence points obtained from the planning.

[0129] Step 4: Set up a nonlinear controller based on the dynamic model and fuzzy algorithm. Use the nonlinear controller to control the amphibious robot according to the optimal land-air cross-domain path. In addition, smoothly switch the trajectory according to the wing folding angle during flight.

[0130] Step 4.1: Construct the position control system function of the amphibious robot based on the dynamic model.

[0131] In one specific implementation, based on the dynamic models (8)-(9), the position control system of the amphibious robot can be expressed as:

[0132]

[0133] in For robot speed, Let M be the robot's acceleration, M be the robot's mass matrix, and U be the robot's control input. Is with The relevant nonlinear disturbance terms are:

[0134]

[0135] The meanings of the variables are explained in formulas (8) and (9).

[0136] Step 4.2: Based on the nonlinear disturbance term in the position control system function, introduce a fuzzy algorithm to construct an adaptive fuzzy controller.

[0137] In one specific implementation, to handle nonlinear interference terms... Introducing fuzzy structures:

[0138]

[0139] Where x is the fuzzy structure input, Output is a fuzzy structure. For fuzzy weights, It is a fuzzy basis function, and

[0140]

[0141] in, and They are respectively and B j Member functions.

[0142] Next, the controller's control objectives are set as follows:

[0143]

[0144] And define the tracking error for

[0145]

[0146] Constructing auxiliary variables

[0147]

[0148] According to (25), there exists an optimal weight vector ω. * satisfy:

[0149]

[0150] Where, ω * By ω ij Composition, and satisfying This is an estimate of ω.

[0151] Then, the approximation error vector ε is defined as follows:

[0152]

[0153] in, And estimation error Represented as

[0154]

[0155] Next, according to (30)-(32), we can obtain

[0156]

[0157] Rearrange (18) into the following form:

[0158]

[0159] Wherein, fuzzy basis function vector satisfy

[0160] To address the approximation error in (31), a weight estimate is designed. With time-varying gain γ ik The update law of (t) as follows:

[0161]

[0162] Where j = 1, 2, ..., n, i = 1, 2, ..., r, k is the upper bound of the fuzzy basis function vector. ωi >0 is a set parameter, Ω ij >0 forms the matrix Ω, and the time-varying gain γ ik (t) forms the matrix Γ(t), and its online update law is as follows:

[0163]

[0164] Where γ0>0, Λ is determined by the positive parameter μ ik constitute.

[0165] Then, according to the update law (35), the estimation error can be written as

[0166] Furthermore, the adaptive fuzzy controller can be designed in the following form:

[0167]

[0168] The parameters must meet the following conditions:

[0169]

[0170] Based on the controller designed in (37) and the update law designed in (35), the system (34) is asymptotically stable, and has:

[0171]

[0172] This invention employs a cross-domain motion verification method for amphibious robots, applicable to the performance verification of the method proposed in this invention. The specific technical solution is as follows:

[0173] Based on the MATLAB platform, a multi-obstacle motion scenario for an amphibious robot was constructed, and the location, size, and number of obstacles were set. According to equations (1)-(7), cross-domain path planning algorithm code was written using MATLAB Function.

[0174] Based on the MATLAB platform, the code for the trajectory planning algorithm for smooth switching of wing folding angle was written based on (20), and constraints such as descent altitude and speed were set.

[0175] Based on the ROS environment and Python language, code for smooth switching of wing folding angle was written, and the designed smooth switching trajectory algorithm was verified on a land-air amphibious robot platform.

[0176] Based on the ROS environment and Python language, we wrote code for an adaptive fuzzy controller to track and control the designed cross-domain path, and verified it on a land-air amphibious robot platform.

[0177] To verify the effectiveness of the method designed in this invention, the above steps can be followed for verification.

[0178] Verification 1: Cross-domain path planning: as shown in the attached document. Figure 2 As shown, the method proposed in this invention achieves energy-optimal land-air cross-domain path planning. When encountering traversable obstacles on the ground, ground movement is prioritized to reduce energy consumption; when obstacles are impassable, aerial movement is considered to improve the robot's adaptability to different environments. Meanwhile, as shown in the attached... Figure 3 As shown, the proposed method achieves rapid convergence of the cost function during the iterative solution process, avoiding getting trapped in local optima.

[0179] Verification 2: Smooth transition trajectory planning: as attached Figure 4 As shown, under the planning method proposed in this invention, a smooth switching trajectory of the wing folding angle was obtained. To verify the effectiveness of the planned trajectory, the upper limit of the descent altitude was set to 0.1m, and aerial mode switching verification was performed on a land-air amphibious robot platform. (See attached diagram) Figure 5 As shown, the robot performs mode switching at a height of 1m according to the planned wing folding angle trajectory. During the switching process, the robot height is always kept above 0.9m. This shows that the proposed wing folding angle smooth switching trajectory planning method has good effect during the mode switching process in the air.

[0180] Verification 3: Adaptive Fuzzy Control Tracking: To verify the effectiveness of the control method proposed in this invention, according to Appendix Figure 1 A scenario was built, and path tracking was verified on a land-and-air amphibious robot platform. (See attached image) Figure 6 As shown, the robot tracks the planned path, and the tracking error converges to 0 within 15 seconds. This verifies the effectiveness of the control method proposed in this invention.

[0181] In summary, the cross-domain motion planning and control method for amphibious robots proposed in this invention has practical applications and good results.

[0182] Example 2:

[0183] Embodiment 2 of the present invention provides a cross-domain motion planning system for a land-air amphibious robot, comprising:

[0184] The constraint determination module is configured to determine the workspace of the amphibious robot according to the task requirements and obtain the operational constraints of the amphibious robot within the workspace.

[0185] The path optimization module is configured to use the particle swarm optimization algorithm to perform path planning based on operational constraints and obtain the optimal land-air cross-domain path.

[0186] The wing switching module is configured to construct a dynamic model based on the dynamic parameters of the amphibious robot, and use the Gaussian pseudospectral method to construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process.

[0187] The motion control module is configured to set a nonlinear controller based on the dynamic model and fuzzy algorithm. The nonlinear controller controls the amphibious robot according to the optimal land-air cross-domain path. In addition, the trajectory is smoothly switched during flight according to the wing folding angle.

[0188] Example 3:

[0189] Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, it implements the steps in the cross-domain motion planning method for amphibious robots as described in Embodiment 1 of the present invention.

[0190] Example 4:

[0191] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the cross-domain motion planning method for a land-air amphibious robot as described in Embodiment 1 of the present invention.

[0192] The steps and methods involved in Examples 2, 3 and 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0193] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0194] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A cross-domain motion planning method for a land-air amphibious robot, characterized in that, Includes the following steps: Determine the workspace of the amphibious robot based on mission requirements and obtain the operational constraints of the amphibious robot within the workspace. The optimal land-air cross-domain path is obtained by using the particle swarm optimization algorithm to perform path planning based on operational constraints. Based on the dynamic parameters of the amphibious robot, a dynamic model is constructed. Using the Gaussian pseudospectral method, with the objective of minimizing the flight altitude drop during mode switching, the specific steps for constructing the smooth switching trajectory of the wing folding angle are as follows: The dynamic model is transformed with the objective of minimizing the decrease in flight altitude during the mode switching process; Set dynamic constraints; A nonlinear programming function with algebraic constraints is constructed based on the transformation dynamic model and dynamic constraints. Solving the nonlinear programming function yields the optimal state sequence for descent altitude; A nonlinear controller is set up based on the dynamic model and fuzzy algorithm. The nonlinear controller is used to control the amphibious robot according to the optimal land-air cross-domain path. In addition, the trajectory is smoothly switched during flight according to the wing folding angle.

2. The cross-domain motion planning method for amphibious robots as described in claim 1, characterized in that, Operational constraints include energy consumption cost constraints, collision cost constraints, and distance cost constraints. Specifically, energy consumption cost constraints are set based on the selection and switching of motion modes, collision cost constraints are set based on the positional relationship between the amphibious robot and obstacles in the workspace, and distance cost constraints are determined based on the segmented path between the amphibious robot and the destination.

3. The cross-domain motion planning method for amphibious robots as described in claim 1, characterized in that, The specific steps for path planning based on runtime constraints using the particle swarm optimization algorithm are as follows: Define the objective function for path planning; Set the velocity and position update laws for each particle; A novel inertial weight is designed based on the consideration of the problem of easily getting trapped in local optima; The optimal land-air cross-domain path is obtained by iteratively solving the objective function based on the update law and the new inertial weights.

4. The cross-domain motion planning method for amphibious robots as described in claim 1, characterized in that, The dynamic model is a dynamic model of the aerial flight mode of the amphibious robot when the wing folding angle is zero.

5. The cross-domain motion planning method for amphibious robots as described in claim 1, characterized in that, Dynamic constraints include folding angle constraints and flight descent constraints for amphibious robots.

6. The cross-domain motion planning method for an amphibious robot as described in claim 1, characterized in that, The specific steps for setting up a nonlinear controller based on the dynamic model and fuzzy algorithm are as follows: Construct the position control system function for the amphibious robot based on the dynamic model; An adaptive fuzzy controller is constructed by introducing a fuzzy algorithm based on the nonlinear disturbance term in the position control system function.

7. A cross-domain motion planning system for an amphibious robot employing the cross-domain motion planning method for amphibious robots as described in any one of claims 1-6, characterized in that, include: The constraint determination module is configured to determine the workspace of the amphibious robot according to the task requirements and obtain the operational constraints of the amphibious robot within the workspace. The path optimization module is configured to use the particle swarm optimization algorithm to perform path planning based on operational constraints and obtain the optimal land-air cross-domain path. The wing switching module is configured to construct a dynamic model based on the dynamic parameters of the amphibious robot, and use the Gaussian pseudospectral method to construct a smooth switching trajectory of the wing folding angle with the objective of minimizing the flight altitude drop during the mode switching process. The motion control module is configured to set a nonlinear controller based on the dynamic model and fuzzy algorithm. The nonlinear controller controls the amphibious robot according to the optimal land-air cross-domain path. In addition, the trajectory is smoothly switched during flight according to the wing folding angle.

8. A computer-readable storage medium, characterized in that, It stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device, representing the cross-domain motion planning method for the amphibious robot according to any one of claims 1-6.

9. A terminal device, characterized in that, It includes a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store multiple instructions, the instructions being adapted to be loaded by the processor and executed by the processor for the cross-domain motion planning method of the land-air amphibious robot according to any one of claims 1-6.

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