Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner
The model predictive control trajectory planner is a popular and effective robot local motion planner. However, it is challenging to satisfy real-time requirements and implement them on embedded platforms due to their high complexity of solving and reliance on optimization solvers. This letter repor...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-11-01
|
| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806241301581 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850138100026572800 |
|---|---|
| author | Xiaoqing Guan Tao Hu Ziang Zhang Yixu Wang Yifan Liu You Wang Jie Hao Guang Li |
| author_facet | Xiaoqing Guan Tao Hu Ziang Zhang Yixu Wang Yifan Liu You Wang Jie Hao Guang Li |
| author_sort | Xiaoqing Guan |
| collection | DOAJ |
| description | The model predictive control trajectory planner is a popular and effective robot local motion planner. However, it is challenging to satisfy real-time requirements and implement them on embedded platforms due to their high complexity of solving and reliance on optimization solvers. This letter reports a lightweight and efficient two-stage solving algorithm for the model predictive control planner. Firstly, the general form of the model predictive control local planning problem was specified and simplified by the motion primitives. Then, a two-stage solving method of multi-layer perceptron pre-solving and particle swarm optimization re-optimizing is developed after splitting the cost function into two pieces. An multi-layer perceptron neural network was designed and trained offline to learn the solution of the model predictive control local planner without considering obstacles after selecting the inputs and outputs. Next, to accomplish obstacle avoidance, the particle swarm optimization algorithm re-optimizes the trajectory based on the outputs of the neural network. The experiment results demonstrate that the multi-layer perceptron-particle swarm optimization algorithm can quickly and accurately solve local planning problems, guiding robots to complete global paths with the same efficiency as expert solvers. The average solving time has been reduced by over 90%, enabling the robot to increase its control frequency or adopt higher-quality complex motion primitives. The multi-layer perceptron-particle swarm optimization algorithm can also be used for various robots and motion primitives, with a wide range of potential applications. |
| format | Article |
| id | doaj-art-c8325ddaacab49aba78ea2afa4a4f614 |
| institution | OA Journals |
| issn | 1729-8814 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj-art-c8325ddaacab49aba78ea2afa4a4f6142025-08-20T02:30:39ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142024-11-012110.1177/17298806241301581Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local plannerXiaoqing Guan0Tao Hu1Ziang Zhang2Yixu Wang3Yifan Liu4You Wang5Jie Hao6Guang Li7 State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, China Rotunbot (Hangzhou) Technology Co. Ltd., Hangzhou, China State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, , Hangzhou, ChinaThe model predictive control trajectory planner is a popular and effective robot local motion planner. However, it is challenging to satisfy real-time requirements and implement them on embedded platforms due to their high complexity of solving and reliance on optimization solvers. This letter reports a lightweight and efficient two-stage solving algorithm for the model predictive control planner. Firstly, the general form of the model predictive control local planning problem was specified and simplified by the motion primitives. Then, a two-stage solving method of multi-layer perceptron pre-solving and particle swarm optimization re-optimizing is developed after splitting the cost function into two pieces. An multi-layer perceptron neural network was designed and trained offline to learn the solution of the model predictive control local planner without considering obstacles after selecting the inputs and outputs. Next, to accomplish obstacle avoidance, the particle swarm optimization algorithm re-optimizes the trajectory based on the outputs of the neural network. The experiment results demonstrate that the multi-layer perceptron-particle swarm optimization algorithm can quickly and accurately solve local planning problems, guiding robots to complete global paths with the same efficiency as expert solvers. The average solving time has been reduced by over 90%, enabling the robot to increase its control frequency or adopt higher-quality complex motion primitives. The multi-layer perceptron-particle swarm optimization algorithm can also be used for various robots and motion primitives, with a wide range of potential applications.https://doi.org/10.1177/17298806241301581 |
| spellingShingle | Xiaoqing Guan Tao Hu Ziang Zhang Yixu Wang Yifan Liu You Wang Jie Hao Guang Li Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner International Journal of Advanced Robotic Systems |
| title | Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner |
| title_full | Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner |
| title_fullStr | Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner |
| title_full_unstemmed | Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner |
| title_short | Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner |
| title_sort | multi layer perceptron particle swarm optimization a lightweight optimization algorithm for the model predictive control local planner |
| url | https://doi.org/10.1177/17298806241301581 |
| work_keys_str_mv | AT xiaoqingguan multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT taohu multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT ziangzhang multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT yixuwang multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT yifanliu multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT youwang multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT jiehao multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner AT guangli multilayerperceptronparticleswarmoptimizationalightweightoptimizationalgorithmforthemodelpredictivecontrollocalplanner |