Self-growing robot based on chemical reaction driving and growth control method thereof

Abstract

The invention discloses a self-growing robot based on chemical reaction driving and a growth control method thereof, and relates to the field of bionic flexible robots, and the self-growing robot comprises a driving storage mechanism, a to-be-grown part of a self-growing robot body and a control module. The driving storage mechanism comprises three chambers which are communicated in sequence, wherein the first chamber is used for storing and releasing a part to be grown; a second reactant is stored in the second chamber; the third chamber stores a first reactant. The first reactant and the second reactant are subjected to chemical reaction in the second chamber to generate gas, and the gas enters the first chamber to drive the part to be grown to turn outwards from the outlet plug to grow. And the control module controls the stepping motor to synchronously release the material. Through an integrated chemical reaction driving mode, dependence on an external air source is eliminated, mooring-free, compact and highly autonomous systems are achieved, and deployment flexibility and motion adaptability of the robot in a narrow and complex environment are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of biomimetic flexible robot technology, and in particular to a self-growing robot based on chemical reaction driven and its growth control method. Background Technology

[0002] Self-growing robots are biomimetic flexible robots that mimic vines. They can extend their length by turning over materials or by additive manufacturing, and have good flexibility. They are suitable for tasks such as detection and inspection in small or restricted environments.

[0003] However, most self-growing robots in related technologies use external air sources or hydraulic drives to achieve material outward growth through positive pressure. This tethered drive structure relies on external equipment such as air compressors and air tanks, resulting in a bulky and poorly portable system. Furthermore, the tethered tubing is prone to tangling or jamming in complex environments, limiting the robot's autonomous deployment and flexible movement in confined spaces.

[0004] Therefore, there is an urgent need for a chemical reaction-driven self-growing robot to solve the problems of insufficient portability and flexibility caused by tethered design, thereby improving the deployment capability and adaptability of self-growing robots in various complex environments. Summary of the Invention

[0005] The purpose of this application is to provide a chemical reaction-driven self-growing robot and its growth control method, which can drive the self-growing robot to increase its length without any external gas source, and integrates the driving mechanism and material storage mechanism into one unit, realizing the tetherless design of the self-growing robot.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a self-growing robot based on chemical reaction driven, comprising: a drive storage mechanism, a part to be grown of the self-growing robot body, and a control module; The drive storage mechanism includes a first chamber, a second chamber, and a third chamber connected in sequence; the control module is connected to the drive storage mechanism. The third chamber is used to store the first reactant; The second chamber is used to store the second reactant, and the first reactant and the second reactant react chemically in the second chamber to generate gas; The first chamber includes a stepper motor, a coupling, a material reel, and an outlet plug; the stepper motor is connected to the material reel via the coupling; the outlet plug is located on the side wall of the first chamber; one end of the part to be grown is fixed to the outlet plug, and the other end is wound around the material reel inside the first chamber; The first chamber is used to drive the part to be grown from the outlet plug outward to grow into a self-growing robotic body based on the gas. The control module is used to control the stepper motor to drive the material roll to rotate in sync with the growth of the part to be grown, so as to release the part to be grown.

[0007] Secondly, this application provides a growth control method for a chemically reaction-driven self-growing robot, comprising: Obtain the state information of the self-growing robot at sampling time k; the state information includes the air pressure value inside the first chamber and the growth length of the self-growing robot; Based on the state information of the self-growing robot at sampling time k, a nonlinear programming optimization problem is constructed and solved using a sequential quadratic programming algorithm to obtain the optimal predictive control input sequence. The nonlinear programming optimization problem includes an objective function, equality constraints, and inequality constraints. The predictive control input sequence includes multiple supply rate control signals. Here, k is a natural number greater than or equal to 1. The first control input in the optimal predictive control input sequence controls the supply rate of the first reactant from the third chamber to the second chamber via a peristaltic pump, and the growth length of the growth robot at sampling time k+1 is obtained by a growth state detection sensor; the time interval between any two sampling times is a preset sampling period. If the growth length of the self-growing robot at sampling time k+1 meets the preset stopping condition, then the growth control is stopped; the preset stopping condition includes that the difference between the growth length of the self-growing robot and the target growth length is less than a preset length threshold. If the growth length of the growing robot at sampling time k+1 does not meet the preset stopping condition, then based on the state information of the self-growing robot at sampling time k+1, the nonlinear programming optimization problem is reconstructed and solved using a sequential quadratic programming algorithm.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a chemical reaction-driven self-growing robot and its growth control method. By integrating chemical reaction driving and material storage functions through a drive and storage mechanism, it solves the tethered design problem caused by the reliance on external gas sources in existing self-growing robots, and realizes untethered autonomous movement of the self-growing robot. Through the chamber layout design of the first, second, and third chambers, the first reactant and the second reactant react chemically in the second chamber to generate gas, which directly drives the part to be grown to turn outwards for growth. This solves the problem of bulky gas source equipment restricting the portability and deployment flexibility of the system, and achieves the technical effect of compact structure, easy carrying and deployment. By controlling the stepper motor in coordination with the growth of the part to be grown, the growth of the material roll is synchronized to drive the rotation of the material roll to release the part to be grown, which solves the problem of mismatch between material release and growth speed during the growth process, and realizes stable and coordinated growth control. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This application provides an overall structural schematic diagram of a chemical reaction-driven self-growing robot prototype, as an embodiment of the present application; wherein, Figure 1 (a) is a side view of a prototype of a chemical reaction-driven self-growing robot; Figure 1 (b) is a top view of a prototype of a chemical reaction-driven self-growing robot; Figure 2 A side view of an integrated design structure of a drive storage mechanism for a chemically reaction-driven self-growing robot, provided as an embodiment of this application; Figure 3 A top view of an integrated design structure of a drive and storage mechanism for a chemically reaction-driven self-growing robot, provided as an embodiment of this application; Figure 4 A schematic diagram of a split design structure for a drive and storage mechanism of a chemical reaction-driven self-growing robot provided in an embodiment of this application; Figure 5 A diagram illustrating the architecture of a self-growing robot control system driven by a chemical reaction, provided as an embodiment of this application. Figure 6 A schematic flowchart illustrating a growth control method for a chemically reaction-driven self-growing robot provided in an embodiment of this application; Figure 7for Figure 6 The flowchart illustrates the steps of solving the problem using the sequential quadratic programming algorithm.

[0011] Reference numerals: 1-Drive storage mechanism; 2-Self-growing robot body; 3-First chamber; 4-Second chamber; 5-Third chamber; 6-Stepper motor; 7-Coupling; 8-Material reel; 9-Outlet plug; 10-Filter screen; 11-Solenoid valve; 12-Air tube; 13-Peristaltic pump; 14-First reactant; 15-Second reactant; 16-First pressure sensor. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] In one exemplary embodiment, such as Figures 1-4 As shown, a self-growing robot based on chemical reaction is provided, including: a drive storage mechanism 1, a part to be grown of the self-growing robot body 2, and a control module.

[0015] The drive storage mechanism 1 includes a first chamber 3, a second chamber 4, and a third chamber 5 connected in sequence; the control module is connected to the drive storage mechanism 1.

[0016] The third chamber 5 is used to store the first reactant 14.

[0017] The second chamber 4 is used to store the second reactant 15, and the first reactant 14 and the second reactant 15 react chemically in the second chamber 4 to generate gas.

[0018] The first chamber 3 includes a stepper motor 6, a coupling 7, a material reel 8, and an outlet plug 9; the stepper motor 6 is connected to the material reel 8 via the coupling 7; the outlet plug 9 is located on the side wall of the first chamber 3; one end of the part to be grown is fixed to the outlet plug 9, and the other end is wound around the material reel 8 inside the first chamber 3.

[0019] The first chamber 3 is used to drive the part to be grown outward from the outlet plug 9 to form a self-growing robot body 2 based on the gas.

[0020] The control module is used to control the stepper motor 6 to drive the material roll 8 to rotate in sync with the growth of the part to be grown, so as to release the part to be grown.

[0021] In another exemplary embodiment of this application, the second chamber 4 is connected to the first chamber 3 via a gas path control module; the gas path control module includes a filter 10 and a solenoid valve 11; the filter 10 is used to prevent water vapor generated by the chemical reaction from entering the first chamber 3; the solenoid valve 11 is connected to the control module, and the control module is also used to control the gas path between the first chamber 3 and the second chamber 4 through the solenoid valve 11.

[0022] As an optional implementation method, such as Figures 2-3 As shown, if the drive storage mechanism 1 is an integrated design, the filter 10 is located inside the side wall of the second chamber 4, one end of the solenoid valve 11 is located inside the second chamber 4 and connected to the filter 10, and the other end of the solenoid valve 11 is located inside the first chamber 3.

[0023] As an optional implementation method, such as Figure 4 As shown, if the drive storage mechanism 1 is a separate design, one end of the filter 10 is located outside the gas outlet above the side wall of the second chamber 4, and the other end of the filter 10 is connected to one end of the solenoid valve 11; the other end of the solenoid valve 11 is connected to the first chamber 3 through the air pipe 12.

[0024] In another exemplary embodiment of this application, the third chamber 5 and the second chamber 4 are connected by a peristaltic pump 13; the peristaltic pump 13 is connected to a control module.

[0025] The control module is also used to control the supply rate of the first reactant 14 delivered from the third chamber 5 to the second chamber 4 via the peristaltic pump 13.

[0026] In another exemplary embodiment of this application, the drive storage mechanism 1 further includes a first pressure sensor 16; the first pressure sensor 16 is installed at the top inside the first chamber 3 for detecting the air pressure inside the first chamber 3 and sending it to the control module.

[0027] In another exemplary embodiment of this application, a growth status detection sensor is installed on the stepper motor 6; the growth status detection sensor is used to detect the growth length and growth speed of the part to be grown in real time and send them to the control module.

[0028] In another exemplary embodiment of this application, the portion to be grown is a flexible tubular material; the flexible tubular material includes a tubular polyethylene plastic film.

[0029] The first reactant 14 and the second reactant 15 are respectively an acidic solution and an alkaline solution that can undergo a chemical reaction to generate gas.

[0030] Based on the same inventive concept, this application also provides a growth control method for implementing the aforementioned chemical reaction-driven self-growing robot. The solution provided by this method is similar to the implementation described in the above-described chemical reaction-driven self-growing robot. Therefore, the specific limitations in one or more embodiments of the growth control method for a chemical reaction-driven self-growing robot provided below can be found in the above-described limitations for chemical reaction-driven self-growing robots, and will not be repeated here.

[0031] In one exemplary embodiment, a growth control method for a chemically reaction-driven self-growing robot is provided, comprising: Step 1: Obtain the state information of the self-growing robot at sampling time k; the state information includes the air pressure value inside the first chamber and the growth length of the self-growing robot.

[0032] Step 2: Based on the state information of the self-growing robot at sampling time k, a nonlinear programming optimization problem is constructed and solved using a sequential quadratic programming algorithm to obtain the optimal predictive control input sequence. The nonlinear programming optimization problem includes an objective function, equality constraints, and inequality constraints. The predictive control input sequence includes multiple supply rate control signals. Here, k is a natural number greater than or equal to 1.

[0033] Step 3: Based on the first control input in the optimal predictive control input sequence, the supply rate of the first reactant delivered from the third chamber to the second chamber is controlled by a peristaltic pump, and the growth length of the growth robot at sampling time k+1 is obtained by a growth status detection sensor; the time interval between any two sampling times is a preset sampling period. The growth status detection sensor is configured to detect physical quantities related to the growth length, including but not limited to angle sensors (e.g., encoders), linear displacement sensors, or vision sensors.

[0034] Step 4: If the growth length of the self-growing robot at sampling time k+1 meets the preset stopping condition, then stop the growth control; the preset stopping condition includes that the difference between the growth length of the self-growing robot and the target growth length is less than a preset length threshold.

[0035] Step 5: If the growth length of the growing robot at sampling time k+1 does not meet the preset stopping condition, then based on the state information of the self-growing robot at sampling time k+1, the nonlinear programming optimization problem is reconstructed and solved using the sequential quadratic programming algorithm.

[0036] In another exemplary embodiment of this application, step 2 specifically includes: Step 201: Initialize the warm start vector to an empty set, and set the initial value of the iteration number j to 1 and the initial value of the step size to 1.

[0037] Step 202: If the warm start vector is an empty set, then based on the state information of the self-growing robot at sampling time k, the optimization variables for the j-th iteration are generated through the forward integral system dynamic equation; the optimization variables include the predicted control input sequence and the corresponding predicted state information sequence.

[0038] The j-th iteration is performed using the following steps: Step 203-1: If the warm start vector is not an empty set, then the warm start vector is used as the optimization variable for the j-th iteration.

[0039] Step 203-2: Calculate the objective function value, objective function gradient, equality constraint value, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the optimization variables in the j-th iteration.

[0040] Step 203-3: Based on the equality-constrained Jacobian matrix and the inequality-constrained Jacobian matrix of the j-th iteration, construct the Hessian matrix of the j-th iteration using the Gauss-Newton method.

[0041] Step 203-4: Based on the Hessian matrix, objective function gradient, equality constraint residual, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the j-th iteration, the nonlinear programming problem is locally linearized, a quadratic programming subproblem of the j-th iteration is constructed and solved, and the search direction of the j-th iteration is obtained.

[0042] Step 203-5: Based on the search direction of the j-th iteration, the step size of the j-th iteration is determined by Armijo backtracking search.

[0043] Step 203-6: Determine whether the step size of the j-th iteration satisfies the sufficient descent condition of the Merit function, and obtain the first judgment result.

[0044] Step 203-7: If the first judgment result is yes, then based on the search direction and step size of the j-th iteration, update the optimization variables of the j-th iteration, and determine whether the iteration termination condition is met to obtain the second judgment result; the iteration termination condition includes the norm of the search direction of the j-th iteration being less than the preset norm convergence threshold and the equality constraint residual of the j-th iteration being less than the preset equality constraint parameter convergence threshold, or reaching the preset maximum number of iterations.

[0045] Step 203-8: If the first judgment result is negative, then reduce the step size of the j-th iteration by a preset backtracking factor, and re-judge whether the step size of the j-th iteration satisfies the sufficient descent condition of the Merit function.

[0046] Step 203-9: If the second judgment result is yes, then terminate the iteration, take the predicted control input sequence in the updated optimization variables of the j-th iteration as the optimal predicted control input sequence, and update the warm start vector based on the updated optimization variables of the j-th iteration.

[0047] Step 203-10: If the second judgment result is negative, calculate the objective function value, objective function gradient, equality constraint value, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the optimization variables for the (j+1)th iteration, and perform the (j+1)th iteration.

[0048] In another exemplary embodiment of this application, the objective function is: .

[0049] in, Let represent the objective function value of the optimization variable in the j-th iteration; Indicates the speed tracking error weight; This represents the predicted growth rate at the (k+i)th prediction time during the j-th iteration; Indicates the target growth rate; Indicates the weight of the supply rate increment; This represents the supply rate increment at the (k+i)-th prediction time during the j-th iteration; Indicates the prediction time domain; This indicates the control time domain.

[0050] The equality constraints are discretized system dynamic equations; the system dynamic equations include pressure dynamic equations, length dynamic equations, and material outward constitutive equations.

[0051] Inequality constraints include flow non-negativity constraints, upper limit constraints on flow, flow rate of change constraints, upper limit constraints on pressure, and lower limit constraints on pressure.

[0052] In another exemplary embodiment of this application, the supply rate of the first reactant delivered from the third chamber to the second chamber is controlled by a peristaltic pump based on the first control input in the optimal predictive control input sequence, specifically including: The first control input in the optimal predictive control input sequence is saturated and limited using the following formula: .

[0053] in, This represents the supply rate at sampling time k after saturation limiting processing; This represents the first control input in the optimal predictive control input sequence obtained at sampling time k. This indicates the maximum output flow rate of the peristaltic pump.

[0054] Based on the supply rate at sampling time k after saturation limiting processing, the supply rate of the first reactant delivered from the third chamber to the second chamber is controlled by a peristaltic pump.

[0055] This application designs a self-growing robot based on chemical reaction drive, which can be driven to grow by the gas generated by two solutions, avoiding dependence on external gas sources. The chemical reaction drive mechanism and the growth material storage mechanism adopt an integrated design, which is compact and realizes the serialless design of the self-growing robot.

[0056] Based on this, and considering the simplified dynamics and chemical reaction relationships of the self-growing robot, as well as potential gas leakage issues in practical applications, a quantitative model of the gas-driven growth process is established, revealing a mapping relationship between the growth rate of the self-growing robot's end effector and the solution concentration and flow rate. Building upon this, a controller based on nonlinear model predictive control is designed to precisely control the growth rate of the self-growing robot. This is achieved through a dual-feedback closed-loop structure, including pressure and velocity feedback loops. The algorithm transforms the NLP problem in the control process into an iterative solution of a sequential quadratic programming problem, ultimately outputting the optimal supply rate of the peristaltic pump.

[0057] The following example illustrates this application using a specific chemical reaction-driven self-growing robot and its growth control process.

[0058] In one exemplary embodiment, a chemical reaction-driven self-growing robot includes a drive storage mechanism and a self-growing robot body; the drive storage mechanism serves as a container for the chemical reaction, providing growth drive for the self-growing robot while storing the materials required for robot growth.

[0059] The self-growing robot body is made of a cylindrical polyethylene plastic film, with the plastic film turned outwards. One end is fixed to the outlet plug of the drive storage mechanism by tape or other means, and the other end is wrapped around the material roll of the drive storage mechanism.

[0060] The driving storage mechanism serves as the main body of the self-growing robot, such as... Figures 2-3As shown, it mainly comprises three chambers. The first chamber is the material storage section, which mainly includes a stepper motor, a coupling, a material reel, and an outlet plug. The stepper motor is connected to the material reel via the coupling, and the material reel is wound with the thin film material required for the growth of the self-growing robot.

[0061] As an alternative implementation, a pressure sensor is installed at the top of the first chamber to sense the pressure inside the first chamber.

[0062] During the self-growing robot's outward growth process, a stepper motor drives a material roll to rotate and release material. Gas generated by a chemical reaction then propels the robot to increase its length. Furthermore, a growth status detection sensor is installed at the stepper motor to monitor the robot's length and growth speed in real time.

[0063] During the outward growth process, the stepper motor rotates in the forward direction to release the thin film material, the solenoid valve closes, and liquid is injected into the second chamber through the peristaltic pump.

[0064] The second chamber, serving as a container for chemical reactions, mainly consists of a second reactant, a peristaltic pump, and other components.

[0065] The second chamber stores the first reactant.

[0066] The second chamber and the first chamber are connected by a filter and a solenoid valve. The filter prevents moisture generated by the chemical reaction from entering the first chamber and causing short circuits in the internal electronic components. The solenoid valve controls the airflow between the first and second chambers. When a stop command is received (sent by the control module when the end effector of the self-growing robot reaches the target position), the peristaltic pump stops injecting the first reactant into the second chamber. However, the remaining first and second reactants still require some time to react, so the airflow can be immediately disconnected by controlling the solenoid valve.

[0067] The first and second reactants are acidic or alkaline liquids involved in the chemical reaction.

[0068] The peristaltic pump connects the second and third chambers and controls the supply rate (flow rate) of the first reactant in the third chamber. During actual operation, the first reactant is added to the second chamber via the peristaltic pump.

[0069] The first reactant and an excess of the second reactant undergo a chemical reaction to produce gases such as carbon dioxide or oxygen (non-toxic, harmless, and pollution-free), thereby driving the growth of the self-growing robot.

[0070] In addition, alternatively, such as Figure 4As shown, if the integration requirement is low, the part storing the growth material and the part carrying out the chemical reaction can be designed separately. The second chamber and the first chamber are connected by a gas tube, a design that can reduce costs to some extent.

[0071] In one exemplary embodiment, taking the reaction of citric acid and baking soda as an example, any two solutions that can generate gas can be used as materials to drive the self-growing robot.

[0072] The model of a pressure-driven self-growing robot is as follows: .

[0073] Where P is the internal gas pressure of the self-growing robot; A is the cross-sectional area of ​​the self-growing robot; and Y is the yield pressure of the thin film material. The coefficient of ductility of the material; denoted as , where is the growth rate at the end of the self-growing robot; n is the power exponent of the robot. is the friction coefficient related to the robot's length; w is the normal force per unit length; L is the total length of the self-growing robot; C is the exponential fitting coefficient; The coefficient of friction is related to curvature. For the robot The length of the curved path segment; For the first The radius of curvature of a curved path segment.

[0074] To establish a chemical reaction-driven model, the following simplifying assumptions are made: The influence of path length is ignored, and the robot is assumed to be relatively short. Ignoring the effect of the curvature, at this time... .

[0075] The following simplified relation can then be obtained: .

[0076] Dividing both sides by the cross-sectional area A, and applying the assumption that n=1, we have: .

[0077] The summary yields: .

[0078] According to the above formula, the growth rate of the robot is directly proportional to the effective pressure, and the proportionality coefficient is the material's ductility.

[0079] This process can be driven by any chemical reaction that produces gas at room temperature. However, considering the reaction products, for environmental protection and to avoid the corrosiveness of some chemical materials, this embodiment uses citric acid (…). Baking soda (baking sodium bicarbonate) reacts with sodium bicarbonate (baking soda) to produce carbon dioxide. .

[0080] in, It represents citric acid; This refers to sodium bicarbonate; This indicates sodium citrate; It represents water; This represents carbon dioxide; the mass ratio of the acid / base and the gaseous substance is 1:3:3. ,in, Indicates the mass of acid. Indicates the mass of alkali. This indicates the amount of carbon dioxide; the acid reacts completely to produce 3 mol of baking soda.

[0081] By maintaining an excess of baking soda, the rate of carbon dioxide formation is controlled by regulating the release rate of the citric acid solution. Let Q be the release rate (supply rate) of the citric acid solution, and c be the molar concentration of the citric acid solution. Then, the carbon dioxide formation rate... for: . The molar supply rate of citric acid.

[0082] Gas leakage is inevitable in real-world systems, so a linear leakage model based on pressure difference is adopted: .

[0083] in, The value represents the molar rate of gas leakage; α is the gas leakage coefficient; P0 is the external atmospheric pressure; and P is the gas pressure inside the robot.

[0084] Consider the ideal gas law as follows: .

[0085] Where P is the gas pressure inside the robot, and V is the gas volume; R is the number of moles of gas; R is the ideal gas constant; T is the absolute temperature.

[0086] Differentiating both sides of the equation, we obtain the following relationship: .

[0087] The rate of change of a robot's volume is directly proportional to its growth rate. .

[0088] in, The initial gas volume to drive the storage mechanism.

[0089] Assuming the pressure changes slowly, this is considered a quasi-static process. Substituting this into the rate of volume change and the rate of carbon dioxide formation, we can obtain: .

[0090] This is a quadratic equation concerning pressure P: .

[0091] To simplify the expression, the following feature parameters are defined: It is a gas production characteristic quantity derived from chemical reaction relationships and the ideal gas law, used to describe the equivalent gas generation capacity corresponding to a unit solution supply rate; For mechanical characteristics: characteristic volumetric flow rate under yield pressure; Characteristic quantity of gas generation: the gas generation power of a chemical reaction. The stoichiometric coefficient for gas production in a chemical reaction represents the number of moles of gas produced for every unit number of moles of solute. In the current embodiment... =3.

[0092] For a quadratic equation, considering that the growth pressure must be positive and greater than the yield pressure, the positive root must be taken: .

[0093] Substituting the pressure into the mechanical equations, we obtain the growth rate: .

[0094] When the gas leakage coefficient α=0, β=0, the velocity expression can be simplified to: .

[0095] In practical control, it is often necessary to calculate the required solution release rate inversely based on the target growth rate. .

[0096] In one exemplary embodiment, the chemical reaction-driven self-growing soft robot model prediction and control system provided in this application, such as... Figure 5 As shown, it consists of two main parts: an MPC (Model Predictive Control) controller and a self-growing robot. It achieves precise tracking control of the robot's growth speed through a dual feedback closed-loop structure.

[0097] The system employs a pressure-velocity dual-feedback closed-loop structure, where pressure feedback is used for state estimation and constraint checking, and velocity feedback is used for tracking error calculation, achieving precise tracking control of the robot's growth rate. Velocity is measured indirectly via an encoder.

[0098] The MPC controller includes an error calculation module, a cost function constraint module, and an SQP (Sequential Quadratic Programming) solver module, which are connected in series. The error calculation module receives the target velocity v. ref With speed feedback signal The difference between the two is calculated as the speed tracking error; the cost function constraint module constructs the optimization objective function and system constraints based on the speed tracking error and the pressure feedback signal P (gas pressure inside the robot); the SQP solver module uses a sequential quadratic programming algorithm to solve the nonlinear optimization problem online and outputs the optimal citric acid solution release rate controlled by the peristaltic pump.

[0099] like Figures 6-7 As shown, a flowchart illustrating the growth control method for a chemically reaction-driven self-growing robot and a flowchart illustrating the solution steps using a sequential quadratic programming algorithm are presented, specifically including the following steps: Step S1: Initialization.

[0100] (1) Initialize the discrete time step index k to 1.

[0101] (2) The control quantity Q from the previous moment k-1 Initializing to 0 indicates that there is no solution flow input before the system starts.

[0102] (3) The robot length L at the previous moment k-1 Initialize to the initial length. The initial length L0 is the robot's natural length when it is not inflated.

[0103] (4) Set the warm start vector Z warm Initialized as an empty set, the warm start vector is used to accelerate the solution of subsequent optimization problems.

[0104] (5) Set the model prediction control parameters, including: prediction time domain N p Control time domain N c Sampling period T s Velocity tracking weight q v And control the incremental weight r.

[0105] (6) Set the system physical constraint parameters, including: the maximum flow rate of the peristaltic pump. Maximum flow rate change Maximum safety pressure Material yield pressure threshold .

[0106] As an optional implementation, the prediction time domain N p The value range is 10-30, and the control time domain Nc The value range is 5-15, and the sampling period T s The value range is 0.01s-0.1s.

[0107] Step S2: Sensor Measurement. At each sampling time, the current state information of the soft robot is acquired through sensors, specifically including: (1) Measure the pressure value P in the air chamber inside the robot using a pressure sensor. k .

[0108] (2) Measure the growth length L of the robot using a length sensor or displacement sensor. k .

[0109] (3) The pressure measurement value P k and length measurement value L k Combined into a state vector The state vector fully describes the system state of the soft robot at the current moment.

[0110] (4) Estimate the current growth rate by differential calculation based on the length measurements at two adjacent sampling times: .

[0111] in, T represents the length measurement at sampling time k-1. s The sampling period is [period]. The estimated velocity value [is described]. Used to calculate speed tracking error.

[0112] Step S3: Construct the NLP problem. Based on the current state measurements and the target reference trajectory, construct a nonlinear programming (NLP) optimization problem. The NLP problem includes three parts: an objective function, equality constraints, and inequality constraints. (a) Objective Function. The objective function is defined as follows: .

[0113] in, Let represent the objective function value of the optimization variable in the j-th iteration; >0 indicates the speed tracking error weight; This represents the predicted growth rate at the (k+i)th prediction time during the j-th iteration; Indicates the target growth rate; >0 indicates the weight of the supply rate increment; This represents the supply rate increment at the (k+i)-th prediction time during the j-th iteration; , Let represent the predicted supply rate at the (k+i)th prediction time during the j-th iteration and the predicted supply rate at the (k+i-1)th prediction time during the j-th iteration, respectively. Indicates the prediction time domain; This represents the control time domain. The objective function has a first term that penalizes the velocity tracking error and a second term that penalizes drastic changes in the control input.

[0114] (ii) Equality Constraints (Dynamic Equations). The equality constraints are discretized system dynamic equations, specifically including: (a) Pressure dynamic equation: .

[0115] Where γ=αRT is the leakage characteristic quantity obtained by combining the gas leakage coefficient α with the gas state parameters R and T, which is used to convert the leakage process from molar quantity form to volumetric flow rate form; c is the citric acid concentration; R is the ideal gas constant; T is the ambient temperature; P0 is the external atmospheric pressure; A is the robot cross-sectional area; V0 is the initial gas volume driving the storage mechanism.

[0116] Pressure dynamic equation constraints: .

[0117] Partial derivative with respect to current pressure: .

[0118] Partial derivative of pressure at the next moment: .

[0119] Partial derivative with respect to length: .

[0120] Partial derivative with respect to flow rate: .

[0121] (b) Length dynamic equation: .

[0122] Length dynamic equation constraints: .

[0123] .

[0124] .

[0125] (c) Constitutive equation of material outward turning (smoothed form): .

[0126] The constitutive equation describes the relationship between the material's outward turning rate and pressure: .

[0127] .

[0128] in, The constraint represents the pressure dynamic equation constraint at step i, that is, the equality constraint on the pressure state P(k+i+1|k), which describes the discrete-time evolution of the pressure inside the air cavity at the prediction time k+i. The length dynamic equation constraint at step i is the equality constraint on the length state L(k+i+1|k), which describes the discrete-time evolution of the growth length of the self-growing robot at the prediction time k+i. The constraint of the material outward constitutive equation at step i represents the equality constraint on the growth rate v(k+i|k), which describes the nonlinear mapping relationship between the material outward velocity and the internal pressure of the air cavity at the predicted time k+i.

[0129] (3) Smooth approximation function: .

[0130] in, The material kinematics coefficient; The yield pressure threshold; It is a smooth approximation of the function max(x, 0). This is a smoothing parameter, with a value range of 10. -6 Up to 10 -4 .

[0131] (iii) Inequality constraints (physical constraints), including: (a) Non-negativity constraint on flow: Because solution pumps can only deliver in one direction.

[0132] (b) Traffic limit constraint: It is limited by the maximum output capacity of the peristaltic pump.

[0133] (c) Flow rate change constraint: This ensures the stability of chemical reactions.

[0134] (d) Pressure upper limit constraint: To prevent the material from cracking; (e) Lower pressure limit constraint: i.e. This ensures that the robot can move continuously.

[0135] (iv) Optimize the state and control inputs of the variables at all prediction times: .

[0136] The dimension of the optimization variable vector is: .

[0137] Step S4: Solve using SQP iteration.

[0138] The Sequential Quadratic Programming (SQP) algorithm is used to iteratively solve the NLP problem constructed in step S3, which specifically includes the following sub-steps: S4.1 Initialize the iteration. If an optimal solution exists from the previous sampling period... Then a warm start strategy will be adopted. The translated point serves as the initial point for the current iteration. Otherwise, generate an initial guess by forward integrating the system dynamics equations. Set the iteration counter j=1.

[0139] S4.2 Calculate the function value and gradient.

[0140] At the current iteration point At this point, calculate the objective function value. and its gradient Equality-constrained residuals and its Jacobian matrix Inequality constraint values and its Jacobian matrix .

[0141] The expression for the gradient of the objective function is: .

[0142] in, Let represent the gradient value of the objective function of the optimization variable in the j-th iteration.

[0143] The partial derivative of velocity with respect to pressure is: .

[0144] The partial derivative with respect to the length state is: .

[0145] The partial derivative with respect to the control input is: .

[0146] Jacobian matrix , The dimension is A real vector.

[0147] The complete form of the equality constraint Jacobian matrix is: .

[0148] Complete inequality constraint Jacobian matrix: .

[0149] in, , , These represent the pressure prediction values ​​at the current time based on the constraints of the pressure dynamic equation at step 0. Next moment's predicted pressure value Length prediction value at current time The partial derivatives; , , These represent the constraints of the first step pressure dynamic equation. , , The partial derivatives; , , They represent the first Step pressure dynamic equation constraint pair , , The partial derivatives of, where It is always equal to 1. and The results are obtained by taking partial derivatives of the pressure dynamic equation with respect to the current pressure and the current length, respectively. , These represent the constraints of the dynamic equations at step 0. , The partial derivatives; , These represent the first step length dynamic equation constraint pairs. , The partial derivatives; , They represent the first Step-length dynamic equation constraints , The partial derivatives of, where It is always equal to -1. It is always equal to 1; , , These represent step 0, step 1, and step 2 respectively. Step pressure dynamic equation constraints on the predicted supply rate at the corresponding time point , , The partial derivatives; and These represent the positive coefficient (+1) and negative coefficient (-1) corresponding to the flow rate change constraint in the Jacobian matrix of the inequality constraint, respectively, which are used to describe the two-sided linear inequality constraint of the supply rate increment at adjacent sampling times.

[0150] S4.3 Construct the Hessian matrix.

[0151] The Hessian matrix of the Lagrangian function is approximated using the Gauss-Newton method: .

[0152] in, Let Jacobian matrix be the residual vector. This indicates the transpose, and δ>0 is a regularization parameter to ensure the matrix is ​​positive definite; Represents the identity matrix.

[0153] S4.4 Construct and solve the QP (Quadratic Programming) subproblem.

[0154] By locally linearizing the NLP problem at the current point, we obtain the QP subproblem: .

[0155] The QP solver is used to solve the above subproblems to obtain the search direction. .

[0156] Specifically, in the j-th iteration, the objective function and constraints in the original nonlinear programming (NLP) problem are locally approximated using the current optimization variable as the linearization point. Specifically, the second-order Taylor expansion of the objective function at the current point is used as the quadratic objective function, where the second-order term is given by the Hessian matrix of the Lagrange function or its approximate matrix, and the first-order term is given by the gradient vector of the objective function.

[0157] At the same time, the equality constraints and inequality constraints are linearized to the first order at the current point to obtain the corresponding linear constraint forms, whose coefficient matrices are composed of the Jacobian matrices of the equality constraints and the Jacobian matrices of the inequality constraints, respectively.

[0158] Based on the above quadratic objective function and linear constraints, a quadratic programming subproblem that is locally equivalent to the original nonlinear programming problem at the current iteration point is constructed.

[0159] Subsequently, a preset quadratic programming solver is invoked to solve the quadratic programming subproblem, thereby obtaining the search direction corresponding to the current iteration step.

[0160] S4.5 line search.

[0161] The step size is determined using Armijo backtracking search. This allows the Merit function to decrease sufficiently. Initial settings =1, if the descent condition is not met, then use the backtracking factor. (Usually taken as 0.5) Reduce the step size until the following condition is met: .

[0162] in, The Merit function combines the objective function and the degree of constraint violation. To fully reduce the parameters.

[0163] S4.6 update iteration point.

[0164] Update and optimize variables based on search direction and step size: .

[0165] Increment the iteration counter j by 1.

[0166] S4.7 Convergence judgment.

[0167] Determine if the convergence condition is met: Search direction norm And the equality constraint residuals ,in and These are the convergence thresholds for the convergence direction and the equality constraint residuals, respectively. The value range is 10 -6 Up to 10 -3 , The value range is 10 -8 Up to 10 -4 The search direction norm is used to determine whether the optimality of the search direction has converged, while the equality constraint residuals correspond to whether the feasibility is satisfied.

[0168] If the convergence condition is met or the number of iterations exceeds the maximum allowed number of iterations. If the iteration terminates, the optimal solution is output. Otherwise, return to step S4.2 and continue iterating.

[0169] Step S5: Output control signal. The optimal solution z obtained from step S-4 Extracting control signals, specifically including: (1) From the optimization variable vector z Extract the first control variable That is, the optimal flow value obtained at the current moment through optimization.

[0170] (2) Perform saturation limiting on the extracted control quantity: .

[0171] in, This represents a saturation function used to limit the control quantity within a preset upper and lower limit range; the saturation limit ensures that the actual output control quantity is within a physically feasible range. (3) The control quantity after the amplitude limit The output is sent to a flow actuator, which controls the solution pump to operate at a flow rate of... A mixture of citric acid and baking soda is injected into the reaction chamber, triggering a chemical reaction that produces carbon dioxide gas, which drives the self-growing robot to move.

[0172] (4) The current optimized solution z Save as warm start vector This is used to accelerate the optimization solution for the next sampling period.

[0173] Step S6: Sampling wait and state update.

[0174] After outputting the control signal, perform sampling wait and state update operations, specifically including: (1) Update historical control quantity: Save the current control quantity as the control quantity at the previous moment, i.e. It is used to calculate the control increment ΔQ in the next sampling period.

[0175] (2) Update historical length value: Save the current length measurement value as the length value at the previous moment, i.e. Used to calculate the velocity estimate in the next sampling period .

[0176] (3) Update the time step index: .

[0177] (4) Waiting for the sampling period T s After the specified time, return to step S2 for the next round of sensor measurement and control calculation. The waiting operation can be implemented via timer interrupt or software delay to ensure the accuracy of the control cycle.

[0178] Steps S2 to S6 are executed cyclically until the control task is completed or the system is shut down. Through a rolling time-domain optimization strategy, the method of this application can respond to state changes and disturbances in real time, achieving precise tracking and control of the growth rate of the self-growing robot.

[0179] In summary, traditional self-growing robots rely on external air or hydraulic power for propulsion, failing to achieve tethered design and resulting in bulky and cumbersome structures. This severely restricts the system's portability and deployment flexibility, hindering its application in confined environments. This application proposes a chemically reaction-driven self-growing robot, where the gas generated by the chemical reaction propels the robot. It requires no external air source, boasts a compact structure, and achieves integrated design and untethered autonomous movement. Furthermore, a growth rate control method based on nonlinear model predictive control is proposed, combined with sequential quadratic programming for iterative optimization. This enables modeling and precise control of typical nonlinear systems, exhibiting stronger robustness and environmental adaptability compared to traditional control methods, and demonstrating better handling of constraints in the environment.

[0180] Therefore, this application opens up new methods for driving self-growing robots and realizing untethered autonomous movement of soft robots.

[0181] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A self-growing robot based on chemical reaction-driven processes, characterized in that, include: Drive storage mechanism, the part to be grown of the self-growing robot body and control module; The drive storage mechanism includes a first chamber, a second chamber, and a third chamber connected in sequence; the control module is connected to the drive storage mechanism. The third chamber is used to store the first reactant; The second chamber is used to store the second reactant, and the first reactant and the second reactant react chemically in the second chamber to generate gas; The first chamber includes a stepper motor, a coupling, a material reel, and an outlet plug; the stepper motor is connected to the material reel via the coupling; the outlet plug is located on the side wall of the first chamber; one end of the part to be grown is fixed to the outlet plug, and the other end is wound around the material reel inside the first chamber; The first chamber is used to drive the part to be grown from the outlet plug outward to grow into a self-growing robotic body based on the gas. The control module is used to control the stepper motor to drive the material roll to rotate in sync with the growth of the part to be grown, so as to release the part to be grown.

2. The self-growing robot based on chemical reaction drive according to claim 1, characterized in that, The second chamber is connected to the first chamber via a gas path control module; the gas path control module includes a filter and a solenoid valve; the filter is used to prevent water vapor generated by the chemical reaction from entering the first chamber; the solenoid valve is connected to the control module, and the control module is also used to control the gas path between the first chamber and the second chamber through the solenoid valve.

3. The chemically reaction-driven self-growing robot according to claim 1, characterized in that, The third chamber is connected to the second chamber via a peristaltic pump; the peristaltic pump is connected to the control module; The control module is also used to control the supply rate of the first reactant delivered from the third chamber to the second chamber via a peristaltic pump.

4. The chemically reaction-driven self-growing robot according to claim 1, characterized in that, The drive storage mechanism also includes a first pressure sensor; the first pressure sensor is installed at the top of the first chamber to detect the air pressure inside the first chamber and send it to the control module.

5. The self-growing robot based on chemical reaction drive according to claim 1, characterized in that, A growth status detection sensor is installed on the stepper motor; the growth status detection sensor is used to detect the growth length and growth rate of the part to be grown in real time and send the data to the control module.

6. The chemically reaction-driven self-growing robot according to claim 1, characterized in that, The part to be grown is a flexible tubular material; the flexible tubular material includes a cylindrical polyethylene plastic film; The first reactant and the second reactant are an acidic solution and an alkaline solution, respectively, which can undergo a chemical reaction to generate gas.

7. A growth control method for a self-growing robot based on chemical reaction-driven growth, characterized in that, The growth control method for the chemical reaction-driven self-growing robot is applied to the control module described in any one of claims 1-6, and the growth control method for the chemical reaction-driven self-growing robot includes: Obtain the state information of the self-growing robot at sampling time k; the state information includes the air pressure value inside the first chamber and the growth length of the self-growing robot; Based on the state information of the self-growing robot at sampling time k, a nonlinear programming optimization problem is constructed and solved using a sequential quadratic programming algorithm to obtain the optimal predictive control input sequence. The nonlinear programming optimization problem includes an objective function, equality constraints, and inequality constraints. The predictive control input sequence includes multiple supply rate control signals. Here, k is a natural number greater than or equal to 1. The first control input in the optimal predictive control input sequence controls the supply rate of the first reactant from the third chamber to the second chamber via a peristaltic pump, and the growth length of the growth robot at sampling time k+1 is obtained by a growth state detection sensor; the time interval between any two sampling times is a preset sampling period. If the growth length of the self-growing robot at sampling time k+1 meets the preset stopping condition, then growth control is stopped; the preset stopping condition includes that the difference between the growth length of the self-growing robot and the target growth length is less than a preset length threshold. If the growth length of the growing robot at sampling time k+1 does not meet the preset stopping condition, then based on the state information of the self-growing robot at sampling time k+1, the nonlinear programming optimization problem is reconstructed and solved using a sequential quadratic programming algorithm.

8. The growth control method for a chemically reaction-driven self-growing robot according to claim 7, characterized in that, Based on the state information of the self-growing robot at sampling time k, a nonlinear programming optimization problem is constructed and solved using a sequential quadratic programming algorithm to obtain the optimal predictive control input sequence, specifically including: The warm start vector is initialized to an empty set, and the initial value of the iteration number j is set to 1, and the initial value of the step size is set to 1; If the warm initiation vector is an empty set, then based on the state information of the self-growing robot at sampling time k, the optimization variables for the j-th iteration are generated through the forward integral system dynamic equation; the optimization variables include the predicted control input sequence and the corresponding predicted state information sequence. If the warm start vector is not an empty set, then the warm start vector will be used as the optimization variable for the j-th iteration; The j-th iteration is performed using the following steps: Calculate the objective function value, objective function gradient, equality constraint value, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the optimization variables in the j-th iteration; Based on the equality-constrained Jacobian matrix and the inequality-constrained Jacobian matrix of the j-th iteration, the Hessian matrix of the j-th iteration is constructed using the Gauss-Newton method. Based on the Hessian matrix, objective function gradient, equality constraint residual, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the j-th iteration, the nonlinear programming problem is locally linearized, a quadratic programming subproblem of the j-th iteration is constructed and solved, and the search direction of the j-th iteration is obtained. Based on the search direction of the j-th iteration, the step size of the j-th iteration is determined by Armijo backtracking search. Determine whether the step size of the j-th iteration satisfies the sufficient descent condition of the Merit function, and obtain the first judgment result; If the first judgment result is yes, then based on the search direction and step size of the j-th iteration, update the optimization variables of the j-th iteration, and determine whether the iteration termination condition is met to obtain the second judgment result; the iteration termination condition includes the norm of the search direction of the j-th iteration being less than the preset norm convergence threshold and the equality constraint residual of the j-th iteration being less than the preset equality constraint parameter convergence threshold, or reaching the preset maximum number of iterations. If the first judgment result is negative, then the step size of the j-th iteration is reduced by a preset backtracking factor, and the step size of the j-th iteration is re-judged to see if it satisfies the sufficient descent condition of the Merit function. If the second judgment result is yes, then the iteration is terminated, the prediction control input sequence in the updated optimization variables of the j-th iteration is taken as the optimal prediction control input sequence, and the warm start vector is updated based on the updated optimization variables of the j-th iteration. If the second judgment result is negative, then calculate the objective function value, objective function gradient, equality constraint value, inequality constraint value, equality constraint Jacobian matrix, and inequality constraint Jacobian matrix of the optimization variables for the (j+1)th iteration, and perform the (j+1)th iteration.

9. The growth control method for a chemically reaction-driven self-growing robot according to claim 8, characterized in that, The objective function is: ； in, Let represent the objective function value of the optimization variable in the j-th iteration; Indicates the speed tracking error weight; This represents the predicted growth rate at the (k+i)th prediction time during the j-th iteration; Indicates the target growth rate; Indicates the weight of the supply rate increment; This represents the supply rate increment at the (k+i)-th prediction time during the j-th iteration; Indicates the prediction time domain; Indicates control over the time domain; The equality constraints are discretized system dynamic equations; the system dynamic equations include pressure dynamic equations, length dynamic equations, and material outward constitutive equations. Inequality constraints include flow non-negativity constraints, upper limit constraints on flow, flow rate of change constraints, upper limit constraints on pressure, and lower limit constraints on pressure.

10. The growth control method for a chemically reaction-driven self-growing robot according to claim 7, characterized in that, Based on the first control input in the optimal predictive control input sequence, the peristaltic pump controls the supply rate of the first reactant from the third chamber to the second chamber, specifically including: The first control input in the optimal predictive control input sequence is saturated and limited using the following formula: ； in, This represents the supply rate at sampling time k after saturation limiting processing; This represents the first control input in the optimal predictive control input sequence obtained at sampling time k. This indicates the maximum output flow rate of the peristaltic pump; Based on the supply rate at sampling time k after saturation limiting processing, the supply rate of the first reactant delivered from the third chamber to the second chamber is controlled by a peristaltic pump.

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