Multi-mission aerial flight dexterous arm trajectory planning method, system and storage medium

Abstract

This application discloses a method, system, and storage medium for trajectory planning of a multi-task aerial dexterous arm. The method includes: establishing a pose change function by comprehensively considering the position and attitude changes of the aerial dexterous arm during task execution; determining the execution order of each task using a composite particle swarm optimization algorithm with the objective of minimizing the value of the pose change function; and determining the motion trajectory of the aerial dexterous arm between two adjacent tasks in the execution order. The method of this application minimizes the attitude disturbance of the multi-rotor UAV, and the calculation results have high accuracy.

Description

Technical Field

[0001] This application relates to the field of aircraft trajectory planning technology, and in particular to a method, system and storage medium for trajectory planning of multi-mission aerial manipulators. Background Technology

[0002] Since scientists proposed in the European Robotics Strategic Research Agenda in 2009 to use improved drones to perform complex and delicate tasks such as measurement, grasping, handling, and assembly, research on aerial robotic arm systems composed of drones and robotic arms has never stopped. Examples include the HYFLIERS (HYbrid Flying-rolling with-snakE-aRm robot for contact inspection) project and the AEROARMS (AErial RObotic system integrating multiple ARMS) project for industrial inspection and maintenance tasks, the ARCAS (Aerial Robotics Cooperative Assembly System) project for aerial assembly and handling tasks, and the AEROWORKS (AErial RObotic WORKerS) project for wind power facility maintenance tasks.

[0003] Multi-rotor drones offer advantages such as simple mechanical structure, low controller design complexity, low vibration, low cost, and ease of operation. Dexterous robotic arms possess super-redundant degrees of freedom, meaning their joint space dimension is far greater than the task space dimension, which can be used to solve problems related to obstacle avoidance, singularity avoidance, limited range of motion, and performance optimization. Aerial dexterous arms combine the characteristics of both multi-rotor drones and dexterous robotic arms. Utilizing aerial dexterous arms to continuously perform multiple high-altitude tasks in a single trip offers advantages such as cost reduction and time savings. During task execution, trajectory planning of the dexterous arm is crucial. Reasonable trajectory planning not only improves task completion efficiency but also ensures that the multi-rotor drone is not subjected to excessive disturbances, thereby improving the stability of task execution. However, current trajectory planning methods cannot yet minimize the disturbances experienced by the multi-rotor drone. Summary of the Invention

[0004] This application provides a method, system, and storage medium for planning the trajectory of a multi-task aerial maneuvering arm, in order to solve the problem of minimizing disturbances to multi-rotor UAVs in existing trajectory planning methods.

[0005] On the one hand, embodiments of this application provide a trajectory planning method for a multi-task aerial dexterous arm, including:

[0006] A pose change function is established by comprehensively considering the position and attitude changes of the aerial dexterous arm during mission execution.

[0007] With the goal of minimizing the value of the pose change function, a composite particle swarm optimization algorithm is used to determine the execution order of each task;

[0008] Determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence.

[0009] On the other hand, embodiments of this application also provide a multi-task aerial flight dexterous arm trajectory planning system, including:

[0010] The function creation module is used to comprehensively consider the position and attitude changes of the aerial dexterous arm during mission execution and to create the pose change function.

[0011] The sequence determination module is used to determine the execution order of each task by minimizing the value of the pose change function using a composite particle swarm optimization algorithm.

[0012] The trajectory planning module is used to determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence.

[0013] On the other hand, embodiments of this application also provide a computer storage medium storing a plurality of computer instructions for causing a computer to execute the above-described method.

[0014] The multi-task aerial maneuvering arm trajectory planning method, system, and storage medium in this application have the following advantages:

[0015] For high-altitude multi-tasking scenarios, this invention plans the operation of an aerial robotic arm system to continuously execute multiple tasks. First, the execution sequence of the high-altitude tasks is determined. Then, the motion trajectories of each joint of the dexterous arm are found to minimize disturbances to the multi-rotor UAV. A further improvement of this invention lies in: fully utilizing the high flexibility of the dexterous arm, considering the continuous execution of multiple high-altitude tasks in a single stroke; and employing a composite particle swarm optimization algorithm that combines the advantages of particle swarm optimization and genetic algorithms, with its inertia factor and learning factor exhibiting self-adjusting characteristics. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0017] Figure 1 A flowchart of a multi-task aerial manipulator trajectory planning method provided in an embodiment of this application;

[0018] Figure 2 The position path of the dexterous arm end effector through the high-altitude operation task point provided in the embodiments of this application;

[0019] Figure 3 The attitude path of the dexterous arm end effector as it passes through the high-altitude operation task point provided in this embodiment of the application;

[0020] Figure 4 A diagram illustrating the joint angle changes provided in an embodiment of this application;

[0021] Figure 5 This diagram illustrates the attitude changes of a multi-rotor UAV as provided in an embodiment of this application. Detailed Implementation

[0022] 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.

[0023] Figure 1 A flowchart illustrating the trajectory planning method for a multi-mission aerial manipulator provided in this application embodiment. This application embodiment provides a trajectory planning method for a multi-mission aerial manipulator, including:

[0024] S100, taking into account the position and attitude changes of the aerial dexterous arm during mission execution, establishes a pose change function.

[0025] For example, when performing multiple tasks sequentially, the end effector of an aerial dexterous arm needs to adjust its position and attitude to adapt to the requirements of each task point. Optimal trajectory planning ensures that the end effector passes through each task point only once. The pose change function established in this application is shown below:

[0026]

[0027] Where F represents the pose change value, m represents the number of task points, and L i and φ iLet α and β represent the position change and attitude change of the end effector of the aerial flying dexterous arm from the i-th task point to the (i+1)-th task point, respectively. α and β represent weighting factors, and α+β=1. By setting reasonable weighting factors, it can be ensured that the position change and attitude change are on the same order of magnitude.

[0028] S110 uses a composite particle swarm optimization algorithm to determine the execution order of each task, with the goal of minimizing the value of the pose change function.

[0029] For example, the method for determining the execution order using the composite particle swarm optimization algorithm is as follows: S111, initialize the number of particles, iteration stopping condition, position of each particle, fitness value, global optimal position, and local optimal position of the particle swarm; S112, determine whether the iteration stopping condition is met. If it is met, output the optimal solution, i.e., the execution order; otherwise, continue the loop; S113, based on the global optimal solution of the particle swarm and the local optimal solution of each particle, self-adjust the values ​​of the learning factor and inertia factor, and update the velocity and position of each particle; S114, perform crossover and mutation operations on the particle swarm; S115, calculate the fitness value, global optimal position, and local optimal position of each particle, and continue to determine whether the iteration stopping condition is met, i.e., return to S112.

[0030] Before executing the above method, it is necessary to first randomly arrange the order of multiple task points and ensure that all orders are exhausted. Then, the sequence of task points with different orders is used as particles, and all particles form a particle swarm for composite particle swarm optimization. In the above algorithm, S111-S113 and S115 are the content of particle swarm optimization (PSO), while S114 is the step of genetic algorithm (GA). This application combines particle swarm optimization and genetic algorithm to form a composite particle swarm optimization algorithm, which utilizes the advantages of genetic algorithm to solve the problem that traditional particle swarm optimization algorithm is prone to getting trapped in local optima.

[0031] S120, determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence.

[0032] For example, S120 specifically includes: determining the joint motion trajectories of the multi-rotor UAV and the dexterous arm based on the kinematic model of the airborne dexterous arm, wherein the kinematic model is:

[0033]

[0034] in, For the motion state of a dexterous arm in flight, v e and w e v represents the linear velocity and angular velocity of the end effector of the dexterous robotic arm, respectively. b and w bThese represent the linear velocity and angular velocity of the multi-rotor UAV, respectively. J represents the joint configuration vector of a dexterous robotic arm. b and J m Let J represent the Jacobian matrices of the multi-rotor drone and the dexterous robotic arm, respectively. g This represents the generalized Jacobian matrix.

[0035] This application embodiment also provides a multi-task aerial flight dexterous arm trajectory planning system, the system comprising:

[0036] The function creation module is used to comprehensively consider the position and attitude changes of the aerial dexterous arm during mission execution and to create the pose change function.

[0037] The sequence determination module is used to determine the execution order of each task by minimizing the value of the pose change function using a composite particle swarm optimization algorithm.

[0038] The trajectory planning module is used to determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence.

[0039] This application also provides a computer storage medium storing a plurality of computer instructions for causing a computer to execute the above-described method.

[0040] Example:

[0041] When using an aerial manipulator to perform multiple high-altitude operations continuously, the manipulator consists of seven links and adjacent joints. The position and attitude information of the detection points are shown in Table 1.

[0042] Table 1 Location and orientation of high-altitude work tasks

[0043]

[0044] First, using a self-regulating composite particle swarm optimization algorithm, the sequence of the end effector of the aerial dexterous arm passing through five high-altitude task points is obtained: W1→W4→W2→W5→W3. Then, based on the kinematic equations of the aerial dexterous arm, the motion trajectories of each joint of the dexterous arm and the attitude perturbation of the multi-rotor UAV are obtained. Figure 2 This demonstrates the expected and actual position paths of the end effector of a manipulator flying in the air as it passes through a high-altitude work site. Figure 3 The desired attitude path and actual attitude path of the end effector of the aerial flying manipulator as it passes through the high-altitude operation task point are shown. Figure 4 The movement trajectories of each joint of the dexterous arm are shown. Figure 5The figure demonstrates the effect of the dexterous arm joint movement on the attitude disturbance of the multi-rotor UAV. As shown in the figure, the aerial dexterous arm accurately reaches each high-altitude task point, passes through each task point exactly once, and minimizes the attitude disturbance of the multi-rotor UAV.

[0045] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0046] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for trajectory planning of a multi-task aerial dexterous arm, characterized in that, include: A pose change function is established by comprehensively considering the position and attitude changes of the aerial dexterous arm during mission execution. With the goal of minimizing the value of the pose change function, a composite particle swarm optimization algorithm is used to determine the execution order of each task; Determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence; The pose change function is: in, F Indicates the pose change value. m Indicates the number of task points. and These represent the end effectors of the aerial flying dexterous arm, which are respectively composed of the first... i The task point to the i +1 change in position and attitude at task points and Indicates the weighting factor; The method for determining the execution order using the composite particle swarm optimization algorithm is as follows: Initialize the particle swarm, including the number of particles, the iteration stopping condition, the position of each particle, the fitness value, the global optimum position, and the local optimum position. Determine whether the iteration stopping condition is met. If it is met, output the optimal solution, i.e., the execution order; otherwise, continue the loop. Based on the global optimal solution of the particle swarm and the local optimal solution of each particle, the values ​​of the self-adjusting learning factor and inertia factor are determined, and the velocity and position of each particle are updated. Perform crossover and mutation operations on the particle swarm; Calculate the fitness value, global optimum, and local optimum for each particle, and then determine whether the iteration stopping condition is met. Determining the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence includes: The joint motion trajectories of the multi-rotor UAV and the dexterous arm are determined based on the kinematic model of the aerial dexterous arm. The kinematic model is as follows: in, The movement state of a dexterous arm in flight. and These represent the linear velocity and angular velocity of the end effector of the dexterous robotic arm, respectively. and These represent the linear velocity and angular velocity of the multi-rotor UAV, respectively. This represents the joint configuration vector of a dexterous robotic arm. and Let represent the Jacobian matrices for multi-rotor drones and dexterous robotic arms, respectively. This represents the generalized Jacobian matrix.

2. A system applying the multi-task aerial maneuvering arm trajectory planning method of claim 1, characterized in that, include: The function creation module is used to comprehensively consider the position and attitude changes of the aerial dexterous arm during mission execution and to create the pose change function. The sequence determination module is used to determine the execution order of each task by employing a composite particle swarm optimization algorithm with the objective of minimizing the value of the pose change function. The trajectory planning module is used to determine the motion trajectory of the aerial manipulator between two adjacent tasks in the execution sequence.

3. A computer storage medium, characterized in that, The computer storage medium stores a plurality of computer instructions, which are used to cause the computer to execute the method of claim 1.

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