Optimal motion planning method and system for looper robot based on improved PSO algorithm

Abstract

This invention discloses an optimal motion planning method and system for an inchworm robot based on an improved PSO algorithm. First, a kinematic model is established, and the forward and inverse kinematic solutions for position and velocity are derived. Gaits such as creeping, rotating, and flipping, as well as obstacle-crossing performance, are analyzed. Second, based on an error tracking model using Cartesian cubic polynomials and joint-space seventh-order polynomials, an energy-optimal planning method constrained by kinematics and dynamics is designed. Mathematical descriptions of robot energy consumption and spatial curve similarity operators are given. Mathematical models of trajectory and path are established. Finally, experiments are conducted to test the robot's motion planning under continuously switching climbing gaits. Through a series of modeling and motion planning processes for the inchworm robot, this invention enables the robot to quickly derive an optimal energy-efficient path during operation, given a known starting point, thus saving energy and effectively improving work efficiency.

Description

Technical Field

[0001] This invention belongs to the field of robot motion trajectory design, specifically relating to an optimal motion planning method and system for inchworm robots based on an improved PSO algorithm. Background Technology

[0002] When working at heights, workers rely on equipment such as work vehicles, but hardware wear and tear and human error can easily lead to accidents, and the work is both physically demanding and inefficient. To address this, climbing robots have emerged. Through pre-programmed mechanized operations, they replace humans in entering dangerous areas, reducing error rates and improving safety. This technology has attracted significant attention due to its high efficiency and stability, and is gradually becoming a key aid in high-altitude operations.

[0003] The application of climbing robots has not only improved operational safety but also driven the innovation and development of high-altitude work technology. Climbing robots integrate cutting-edge technologies in mechanics, electronics, sensing, communication, automation, and computer science, representing the culmination of multidisciplinary innovation. While the development faces numerous challenges in harsh working environments, their broad application prospects are driving rapid technological advancement.

[0004] Based on the operational scenario, climbing robots are subdivided into two categories: pole climbing and wall climbing. The inchworm robot involved in this application belongs to the pole climbing series. The core focus is on optimizing the robot's movement strategy, striving to minimize energy consumption and improve energy efficiency. Essentially, this involves the optimal motion planning of the robot's energy consumption from the starting point to the target point. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an optimal motion planning method and system for inchworm robots based on an improved PSO algorithm.

[0006] In a first aspect, the present invention provides an optimal motion planning method for an inchworm robot based on an improved PSO algorithm, comprising the following steps:

[0007] Step 1: Construct the kinematic model of the inchworm robot using the DH method;

[0008] Step 2: Based on the kinematic model, analyze the obstacle-crossing capabilities of the inchworm robot;

[0009] Step 3: Based on the kinematic model, the inchworm robot is dynamically modeled using the Lagrange method based on DH parameters to obtain its dynamic expression;

[0010] Step 4: Perform motion planning for the inchworm robot, specifically:

[0011] Step 4-1: Optimize robot motion energy consumption;

[0012] Step 4-2: Construct an error tracking framework based on Cartesian space cubic polynomial and joint space seventh polynomial programming;

[0013] Step 4-3: Perform trajectory planning on the robot based on the continuous acceleration curve in joint space to obtain its trajectory of displacement, velocity, acceleration, and jerk.

[0014] Step 4-4: In Cartesian space, plan a continuous path of motion curves to generate a feasible trajectory for efficient robot operation;

[0015] Steps 4-5: Construct a comprehensive model for optimizing robot energy consumption;

[0016] Step 5: Use the improved PSO algorithm to solve for the path with the lowest energy consumption;

[0017] The improved PSO algorithm includes an improvement to address the inefficiency caused by excessively long search times, wherein the improvement uses an adaptive adjustment strategy to dynamically control the inertia weight value.

[0018] as well as,

[0019] To address the low accuracy caused by being prone to getting stuck in local optima, the second improvement involves introducing a taboo region from the TS algorithm, trading space complexity for time complexity.

[0020] Secondly, this invention provides an optimal motion planning system for an inchworm robot based on an improved PSO algorithm, comprising:

[0021] The kinematic model construction module uses the DH method to construct the kinematic model of the inchworm robot;

[0022] The obstacle-crossing analysis module analyzes the inchworm robot's obstacle-crossing capabilities based on the kinematic model.

[0023] The dynamic model construction module uses the Lagrange method based on DH parameters to perform dynamic modeling of the inchworm robot based on the kinematic model, and obtains its dynamic expression;

[0024] The motion planning module includes optimizing robot motion energy consumption; constructing an error tracking framework based on Cartesian space cubic polynomial and joint space seventh polynomial planning; performing trajectory planning for the robot based on continuous accelerometer curves in joint space to obtain its displacement, velocity, acceleration, and accelerometer trajectory; planning a continuous path of motion curves in Cartesian space to generate a feasible trajectory for efficient robot operation; and constructing a comprehensive model for optimizing robot energy consumption.

[0025] The minimum energy consumption path solving module uses an improved PSO algorithm to solve for the minimum energy consumption path; the improved PSO algorithm includes an improvement to address the inefficiency caused by excessive search time, which uses an adaptive adjustment strategy to dynamically adjust the inertia weight value.

[0026] as well as,

[0027] To address the low accuracy caused by being prone to getting stuck in local optima, the second improvement involves introducing a taboo region from the TS algorithm, trading space complexity for time complexity.

[0028] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of optimal motion planning for an inchworm robot based on an improved PSO algorithm.

[0029] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of optimal motion planning for an inchworm robot based on an improved PSO algorithm.

[0030] The beneficial effects of this invention are as follows: Through a series of modeling and motion planning of the inchworm robot, this invention enables the inchworm robot to quickly obtain an efficient path with optimal energy consumption when the starting point is known. This not only saves energy and extends working time during high-altitude operations, but also effectively improves its working efficiency. Attached Figure Description

[0031] Figure 1 This is a simplified structural diagram of the inchworm robot according to an embodiment of this application;

[0032] Figure 2 This is a parameter diagram of the inchworm robot according to an embodiment of this application;

[0033] Figure 3 This is a description of the space curves in the Cartesian coordinate system according to the embodiments of this application;

[0034] Figure 4 This illustrates the relationship between energy consumption and n in an embodiment of this application.

[0035] Figure 5 This is a flowchart of the algorithm in an embodiment of this application;

[0036] Figure 6 This is a schematic diagram illustrating different climbing paths in embodiments of this application. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and examples.

[0038] This application discloses an optimal motion planning method for inchworm robots based on the PSO algorithm, particularly addressing the motion energy consumption problem of the robot's gait. Based on the "error tracking" model of Cartesian cubic polynomial and joint space seventh polynomial, an energy-optimal planning method constrained by kinematics and dynamics is designed to achieve automatic control of climbing.

[0039] In this application, the energy consumption of a inchworm robot in continuous peristaltic and linear gait was investigated. Considering the robot's kinematic and dynamic constraints, and with minimum energy consumption as the optimization objective, an attempt was made to solve the robot's optimal trajectory based on an improved adaptive particle swarm optimization algorithm.

[0040] The optimal motion planning method for inchworm robots based on the improved PSO algorithm provided in this application includes the following steps:

[0041] Step 1: Use the DH method to construct the kinematic model of the inchworm robot, and integrate velocity kinematics and position kinematics analysis to achieve a comprehensive and detailed kinematic description of the inchworm robot.

[0042] Step two: After obtaining its kinematic model in step one, the inchworm robot's ability to overcome obstacles is analyzed. By considering parameters such as the longitudinal radius of the gripper, the height of the gripper to the adjacent joint, and the length of the link between the joints, the intrinsic relationship between the height and width of the obstacle that can be crossed is revealed.

[0043] Step three: Based on the kinematic model established in step one, the inchworm robot is further modeled using the Lagrange method based on DH parameters to obtain its dynamic expression. A prerequisite for comprehensively obtaining the torques of each joint is the accurate calculation of its displacement, angular velocity, and angular acceleration trajectories.

[0044] Step four: After establishing a complete kinematic and dynamic model of the robot through the above steps and analyzing its obstacle-crossing performance, perform motion planning for the robot.

[0045] First: Optimize the robot motion energy consumption analysis and introduce a more intuitive mathematical expression;

[0046] Second: Construct an "error tracking" framework based on Cartesian space cubic polynomial and joint space seventh polynomial programming. To this end, a spatial curve similarity operator needs to be designed to accurately match path features;

[0047] Third: Perform trajectory planning on the robot based on the continuous acceleration curve in the joint space to obtain its trajectory of displacement, velocity, acceleration, and jerk.

[0048] Fourth: In Cartesian space, plan a continuous path of motion curves to generate a feasible trajectory for efficient robot operation;

[0049] Fifth: Combining the results of steps three and four, construct a comprehensive model for optimizing robot energy consumption.

[0050] Step five: After obtaining the motion energy consumption model in step four, the pursuit of the ideal path for energy optimization essentially involves finding the optimal solution within a complex high-dimensional nonlinear space. This application selects the PSO algorithm as the solution strategy to accurately locate the path with the lowest energy consumption.

[0051] For solving high-dimensional nonlinear problems, the traditional PSO algorithm suffers from two major drawbacks: inefficiency due to excessively long search time and low accuracy due to being trapped in local optima. To address the former, an adaptive adjustment strategy is used to dynamically control the value of ω to overcome this deficiency; for the latter, the taboo region from the TS (Tabu search) algorithm is introduced, trading space complexity for time complexity to solve this problem.

[0052] In a preferred example, in step one, the kinematic model of the inchworm robot is constructed using the DH method, and a simplified structural diagram of the inchworm robot is shown below. Figure 1 As shown.

[0053] In robot operation, if the front gripper S2 remains stationary, the velocity of the robot's end effector can be expressed as: In the formula, J is the Jacobian matrix. The joint angular velocity, These are the linear velocity and angular velocity of the robot's end effector relative to the base coordinate system, respectively.

[0054] The Jacobian matrix is ​​represented as: Considering the inverse kinematics of velocity, for a general multi-degree-of-freedom robot, the inverse kinematics of velocity can be expressed as: When the robot's front gripper S2 is fixed, following the above equation and differentiating the inverse kinematics of the position, we obtain:

[0055]

[0056] Among them, v j1 v j2 v j3 v j4 and v j5 These represent the rotational speeds of each joint, v j234 It is the sum of the rotational speeds of joints J2 to J4.

[0057] The above equation maps the swing leg velocity to the joint velocity. When the rear gripper S1 is fixed, utilizing the robot's symmetry characteristics, the target pose is first transformed to the rear gripper coordinate system, and then the solution is obtained according to the previous equation. This completes the kinematic modeling of the robot.

[0058] In a preferred example, step two involves analyzing the inchworm robot's obstacle-crossing capabilities after obtaining its kinematic model in step one.

[0059] Obstacles are indicated by gray rectangles. The inchworm robot parameters are as follows: Figure 2 As shown. Let the height of the obstacle be h, the width be w, the longitudinal radius of the gripper be R, the height from the gripper to joint J2 be a0, the length of the link from joint J3 to joint J4 be a1, the included angle of joint J3 be q1, and the radius of the joint sleeve be e. Then the relationship between the height and width of the obstacle that the robot can overcome is as follows:

[0060]

[0061] When the robot's torso joints are fully open and extended into a straight line, the obstacle that the robot can cross is the widest. Based on the actual robot, i.e., a0 = 265mm, a1 = 296mm, r = 143mm, e = 46mm, q1 = 0mm, substituting into the left side of the equation, we get w = 306mm. Substituting into the right side of the equation, we get the relationship between h and w.

[0062] In a preferred example, step three involves further robot dynamics modeling based on the kinematic model from step one. To ensure consistency with the kinematic modeling system, a Lagrangian method based on DH parameters is selected to construct the dynamics model.

[0063] If the robot consists of L joints connected in series, then the dynamic expression can be obtained by differentiating the Lagrange equation:

[0064]

[0065] In the formula, the torque is composed of inertial, centrifugal, Coriolis, and gravitational terms. τ i q i These are the generalized torque and displacement of the i-th joint, respectively.

[0066] In actual calculations, since there are L joints in total, and adjacent joints interact with each other, it is necessary to derive and obtain the values ​​of each part one by one. The prerequisite for obtaining the torque of each joint is to accurately calculate the trajectory of its displacement, angular velocity, and angular acceleration.

[0067] Dynamic modeling needs to distinguish between the robot's upper and lower clamping situations. Given the symmetry of the robot design in this application embodiment, the modeling process for the other clamping situation is similar, except that the derivation order of the joints and links is reversed.

[0068] In a preferred example, step four involves motion planning for the robot after establishing a complete kinematic and dynamic model of the robot through the above steps and analyzing its obstacle-crossing performance.

[0069] First, optimize the energy consumption analysis of robot motion by introducing a more intuitive mathematical representation. The energy consumption of robot motion can be roughly represented by the following three parts: the energy consumption when the motor joint torque is in the same direction as the robot's motion is positive work, the energy consumption when the direction is opposite is negative work, and the energy consumption from motor heating is heat loss.

[0070] Because the uncertainty of heat loss complicates the calculation of total energy consumption, this embodiment directly uses a more intuitive mathematical description of energy consumption, as shown in the following formula:

[0071]

[0072] In the formula, t s , t e These represent the start and end times of the robot's motion, respectively, and τ is the joint torque.

[0073] Second, within the "error tracking" framework for constructing Cartesian cubic polynomial and joint-space septonic polynomial programming, it is necessary to evaluate the similarity between the planned path and the actual motion trajectory.

[0074] Furthermore, if the error between the two remains within a preset threshold, it is assumed that their energy consumption levels are similar, and this guides the algorithm's search strategy accordingly. To this end, this application defines a quantitative index for spatial curve similarity. For two curves L1 and L2 composed of N discrete points, their similarity can be characterized by calculating the sum of the squares of the Euclidean distances between these points, thereby accurately measuring the spatial proximity between the curves:

[0075]

[0076] In the formula, Let be the spatial coordinates of the i-th point on curve L1. Let be the spatial coordinates of the i-th point on curve L2.

[0077] Third, the embodiments of this application adopt a segmented seventh-order polynomial programming strategy to optimize the robot joint space, ensuring a smooth transition of the jerk curve and effectively suppressing motor resonance.

[0078] Furthermore, we set the i-th joint at the beginning t j and the end time The positions are respectively and Speed ​​parameters are and acceleration is and The jerk is then and Based on the above conditions, this application comprehensively defines the boundary constraints of robot joint spatial motion to achieve precise control:

[0079]

[0080] Given that the above equation contains eight constraints, the polynomial needs at least eight independent coefficients to ensure the existence of a valid solution. Therefore, a seventh-degree polynomial is chosen to describe the joint displacements within the interval:

[0081] q i (t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 +a6t 6 +a7t 7

[0082] To solve for the eight coefficients, the boundary conditions are transformed into matrix equations. By setting reasonable time matrices and boundary conditions, the polynomial parameters can be directly solved.

[0083] Fourth, the starting and ending points P of the robot's motion in space are known. start (X start ,Y start Z start ), P end (X end ,Y end Z end If a suitable sample point P is selected... insert (X insert ,Y insert Z insert Then, we obtain the three-point equation of plane θ:

[0084]

[0085] To ensure that the path connecting three points in a plane is both smooth and continuous, using a polynomial of at least cubic degree is a fundamental requirement. In a specific plane θ, if it is known that the path passes through P... start P insert P end The exact locations p of the three points were determined, and the slopes at these three points were further clarified. Then, cubic polynomial interpolation can be used to accurately construct a smooth curve for the entire path, such as... Figure 3 As shown.

[0086] Fifth, after obtaining the joint trajectory and motion path, the torque of each joint can be calculated using the following formula:

[0087]

[0088] In the formula, f is the implicit function of the Lagrange dynamics recursion, and g is the function of q. i (t), The four items are represented by t. all q i (t) is the transformed function; therefore, the energy consumption of the i-th joint can be described as:

[0089]

[0090] Among them, the planning of the Cartesian space path and the joint space trajectory, q i (t) can be obtained from P insert R max α To describe it. Therefore, the solution model ultimately transforms into:

[0091]

[0092] In the formula, Q is the robot's workspace area, d is a fixed distance coefficient, and ε is a real number.

[0093] From the above model, it can be inferred that if a suitable t is selected during joint space trajectory planning... all The entire solution process then involves optimizing the parameters of the path curve in the Cartesian space.

[0094] Step 5: After obtaining the motion energy consumption model through Step 4, the ideal path to pursue energy consumption optimization is essentially to explore the optimal solution in a complex high-dimensional nonlinear space.

[0095] This embodiment uses the PSO algorithm as the solution strategy to accurately locate the path with the lowest energy consumption. The PSO algorithm is based on a population X = (x1,...x2) of N particles in a T-dimensional space. i ,...x M The position of the i-th particle is X. i =(x i1 ,...x iT The velocity of the i-th particle is V. i =(v i1 ,...v iT The global extremum of the population is B. ω =(b ω1 ,...b ωT The individual extreme value of the particle is B. i =(b i1 ,...b iT Particle x i Update speed and position according to the following formula:

[0096]

[0097] In the formula, i = 1,...,N, k = 1,...,T, where i is the population size and k is the number of evolutions. r1 and r2 are random numbers between 0 and 1, s1 and s2 are individual and group learning factors, respectively, and ω is the inertia weight value.

[0098] For solving high-dimensional nonlinear problems, the two major drawbacks of the traditional PSO algorithm are its low efficiency due to excessively long search time and its low accuracy due to being easily trapped in local optima.

[0099] (1) To address the inefficiency caused by excessively long algorithm search time, the core issue lies in balancing the breadth of search directions with the precision of the step size. This application innovatively introduces the sigmoid activation function from neural networks to dynamically define ω, aiming to achieve adaptive adjustment of the inertia weights of each particle. This retains the advantage of rapid convergence, enhances the flexibility of the search process, and effectively shortens the algorithm's execution time. The sigmoid function is shown in the following equation:

[0100]

[0101] This strategy encourages increasing the value of ω to enhance global exploration capabilities when the value of f(k-1)-f(k) is large, meaning the current solution is far from reaching a local optimum. Conversely, when the value of f(k-1)-f(k) decreases, it means that a local optimum has been approached. At this time, the rapid decay characteristic of the sigmoid function is used to appropriately decrease the value of ω for fine adjustment and to enhance the accuracy of local search.

[0102] (2) To address the issue of low accuracy caused by the algorithm easily getting trapped in local optima, the step size needs to be increased to encourage particles to escape the local optima trap. However, this contradicts the principle of fine-grained search. Therefore, the taboo region in the Tabu search algorithm is introduced to solve this problem by trading the algorithm's space complexity for time complexity.

[0103] This application defines the concept of a forbidden region as: a region containing the locally optimal solution X found in the search. i =(x i1 ,...x iT Let O be the geometric center of space. In the search process, the average search step size η is equal to the radius r. The feasible solution space that satisfies the following equation is the taboo region:

[0104] (xx i1 ) 2 +...+(xx iT ) 2 ≤r 2

[0105] like Figure 4As shown in the simulation, when n is set to 10, the algorithm achieves a good balance between convergence speed and solution accuracy. In summary, the algorithm flowchart is as follows: Figure 5 As shown.

[0106] Based on the same inventive concept as the inchworm robot optimal motion planning method in the foregoing embodiments, this application provides an inchworm robot optimal motion planning system based on an improved PSO algorithm, including:

[0107] The kinematic model construction module uses the DH method to construct the kinematic model of the inchworm robot;

[0108] The obstacle-crossing analysis module analyzes the inchworm robot's obstacle-crossing capabilities based on the kinematic model.

[0109] The dynamic model construction module uses the Lagrange method based on DH parameters to perform dynamic modeling of the inchworm robot based on the kinematic model, and obtains its dynamic expression;

[0110] The motion planning module includes optimizing robot motion energy consumption; constructing an error tracking framework based on Cartesian space cubic polynomial and joint space seventh polynomial planning; performing trajectory planning for the robot based on continuous accelerometer curves in joint space to obtain its displacement, velocity, acceleration, and accelerometer trajectory; planning a continuous path of motion curves in Cartesian space to generate a feasible trajectory for efficient robot operation; and constructing a comprehensive model for optimizing robot energy consumption.

[0111] The minimum energy consumption path solving module uses an improved PSO algorithm to solve for the minimum energy consumption path; the improved PSO algorithm includes an improvement to address the inefficiency caused by excessive search time, which uses an adaptive adjustment strategy to dynamically adjust the inertia weight value.

[0112] as well as,

[0113] To address the low accuracy caused by being prone to getting stuck in local optima, the second improvement involves introducing a taboo region from the TS algorithm, trading space complexity for time complexity.

[0114] In another embodiment, a computer device is provided, which may be a server. This computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement an optimal motion planning method for the inchworm robot.

[0115] Furthermore, to verify the effectiveness of the proposed method, three effective paths, one close to and one far from the optimal path, were selected for comparative testing, denoted as paths 1, 2, and 3, respectively. Figure 6 The diagrams show different climbing paths, and the energy consumption and maximum torque values ​​within the same time period are shown in the table below. The path obtained by the planning method of this invention has the lowest energy consumption.

[0116]

[0117] In summary, this application has the following technical effects:

[0118] 1. The DH method is used to perform kinematic modeling of the inchworm robot. In order to correspond with the kinematic modeling, the Lagrangian method based on DH parameter representation is used for robot dynamic modeling to more accurately derive the specific torque information of each joint of the robot.

[0119] 2. To address the two major drawbacks of the traditional PSO algorithm—namely, low efficiency due to excessively long search time and low accuracy due to being trapped in local optima—a sigmoid function and a forbidden region are introduced to solve these problems and improve the accuracy of motion planning.

[0120] 3. After defining the taboo region, the optimal number of search steps is obtained by evaluating the relationship curve between the number of search steps, convergence speed, and solution accuracy in the algorithm, thereby improving the feasibility of motion planning;

[0121] 4. By comparing the obtained optimal path with other feasible paths in the same motion time, it is proved that the obtained optimal path has the least energy consumption, thus proving the feasibility of the motion planning method of this application.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimal motion planning method for an inchworm robot based on an improved PSO algorithm, characterized in that, Includes the following steps: Step 1: Construct the kinematic model of the inchworm robot using the DH method; Step 2: Based on the kinematic model, analyze the obstacle-crossing capabilities of the inchworm robot; Step 3: Based on the kinematic model, the inchworm robot is dynamically modeled using the Lagrange method based on DH parameters to obtain its dynamic expression; Step 4: Perform motion planning for the inchworm robot, specifically: Step 4-1: Optimize robot motion energy consumption; Step 4-2: Construct an error tracking framework based on Cartesian space cubic polynomial and joint space seventh polynomial programming; Step 4-3: Perform trajectory planning on the robot based on the continuous acceleration curve in joint space to obtain its trajectory of displacement, velocity, acceleration, and jerk. Step 4-4: In Cartesian space, plan a continuous path of motion curves to generate a feasible trajectory for efficient robot operation; Steps 4-5: Construct a comprehensive model for optimizing robot energy consumption; Step 5: Use the improved PSO algorithm to solve for the path with the lowest energy consumption; The improved PSO algorithm includes an improvement to address the inefficiency caused by excessively long search times, wherein the improvement uses an adaptive adjustment strategy to dynamically control the inertia weight value. as well as, To address the low accuracy caused by being prone to getting trapped in local optima, an improvement is proposed. This improvement introduces a taboo region from the TS algorithm, trading algorithm space complexity for time complexity.

2. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 1, characterized in that, In step 1, velocity kinematics and position kinematics analysis are combined to achieve a comprehensive and detailed kinematic description of the inchworm robot.

3. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 1 or 2, characterized in that, In step 2, the intrinsic relationship between the height and width of the obstacle crossing is determined by considering the longitudinal radius of the gripper, the height of the gripper to the adjacent joint, and the length of the connecting rod between the joints.

4. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 1, characterized in that, The motion energy consumption in step 4-1 includes the energy consumption when the motor joint torque is in the same direction as the robot's motion, the energy consumption when the direction is opposite, and the energy consumption caused by motor heating.

5. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 1 or 4, characterized in that, In step 4-2, for two curves composed of multiple discrete points, their similarity is characterized by calculating the sum of squares of the Euclidean distances between these discrete points, thereby accurately measuring the spatial proximity between the curves and obtaining the error tracking framework accordingly.

6. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 5, characterized in that, Step 4-3 employs piecewise seventh-order polynomial programming to ensure a smooth transition of the jerk curve and suppress motor resonance.

7. The optimal motion planning method for inchworm robots based on the improved PSO algorithm according to claim 6, characterized in that, The adaptive adjustment strategy specifically involves introducing the sigmoid activation function from the neural network to dynamically define the inertia weight value.

8. An optimal motion planning system for an inchworm robot based on an improved PSO algorithm, characterized in that, include: The kinematic model construction module uses the DH method to construct the kinematic model of the inchworm robot; The obstacle-crossing analysis module analyzes the inchworm robot's obstacle-crossing capabilities based on the kinematic model. The dynamic model construction module uses the Lagrange method based on DH parameters to perform dynamic modeling of the inchworm robot based on the kinematic model, and obtains its dynamic expression; The motion planning module includes optimizing robot motion energy consumption and constructing an error tracking framework based on Cartesian space cubic polynomial and joint space seventh polynomial planning. The robot's trajectory is planned based on the continuous acceleration curve in joint space to obtain its displacement, velocity, acceleration, and jerk trajectory; in Cartesian space, a continuous path of motion curve is planned to generate a feasible trajectory for efficient robot operation; and a comprehensive model for optimizing robot energy consumption is constructed. The minimum energy consumption path solving module uses an improved PSO algorithm to solve for the minimum energy consumption path; the improved PSO algorithm includes an improvement to address the inefficiency caused by excessive search time, which uses an adaptive adjustment strategy to dynamically adjust the inertia weight value. as well as, To address the low accuracy caused by being prone to getting trapped in local optima, an improvement is proposed. This improvement introduces a taboo region from the TS algorithm, trading algorithm space complexity for time complexity.

9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of optimal motion planning for the inchworm robot based on the improved PSO algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of optimal motion planning for the inchworm robot based on the improved PSO algorithm as described in any one of claims 1 to 7.

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