基于随机重启的机器人高斯过程运动规划*

doi:10.16731/j.cnki.1671-3133.2024.12.007

现代制造工程 ›› 2024, Vol. 531 ›› Issue (12): 54-60.doi: 10.16731/j.cnki.1671-3133.2024.12.007

基于随机重启的机器人高斯过程运动规划^*

袁绪清¹, 魏媛媛², 王耀力¹, 常青¹, 付世沫²

1 太原理工大学电子信息与光学工程学院,晋中 030600;
2 太原供水设计研究院有限公司,太原 030024

收稿日期:2024-04-07 出版日期:2024-12-18 发布日期:2024-12-24
通讯作者: 王耀力,副教授,硕士研究生导师,主要研究方向为机器视觉、计算智能与最优化建模、无线传感器网络。
作者简介:袁绪清,硕士研究生,主要研究方向为机器视觉与计算智能。魏媛媛,本科,主要研究方向为工业通用技术及设备、建筑科学与工程、自动化技术。常青,副教授,硕士研究生导师,主要研究方向为嵌入式系统、人机视觉分析与处理、信息系统设计理论。付世沫,本科,主要研究方向为工业通用技术及设备、建筑科学与工程、自动化技术。E-mail:yuanxq0214@163.com
基金资助:
^*山西省重点研发项目(201903D321003);太原供水设计研究院有限公司项目(RH2000005391);山西省自然科学基金项目(201801D121141)

Gaussian process motion planning for robots based on random restarts

YUAN Xuqing¹, WEI Yuanyuan², WANG Yaoli¹, CHANG Qing¹, FU Shimo²

1 College of Optoelectronics,Taiyuan University of Technology,Jinzhong 030600,China;
2 Taiyuan Water Supply Design and Research Institute Co.,Ltd.,Taiyuan 030024,China

Received:2024-04-07 Online:2024-12-18 Published:2024-12-24

摘要/Abstract

摘要： 针对高斯路径运动规划(GPMP2)算法应用于移动机器人时,在复杂障碍物环境中易陷入局部最优和避障性能不佳的问题,提出一种基于随机重启和避障改进的(GPMP2-SROAI)方法。首先,引入协变哈密尔顿优化(CHOMP)算法中的随机重启机制对轨迹施加扰动,使其跳出局部最优,提高轨迹优化的效率和鲁棒性;随后,引入基于障碍函数的模型预测控制(MPC-CBF)方法,在优化过程中通过预测机器人的运动范围以避免碰撞。仿真结果表明,改进后的规划成功率达到了92.6 %,较GPMP2提高了24.9 %,路径最短概率提高了12.5 %,平均平滑度提高了4.8 %,与主流算法进行对比也取得了更好的轨迹规划质量,轨迹更加平滑且避障效果更佳。

关键词: 路径规划, 随机重启, 机器人避障, 因子图优化

Abstract: In complex obstacle environments,mobile robots using the Gaussian Path Motion Planning (GPMP2) algorithm suffer from the problems of falling into local optimums and poor obstacle avoidance performance,a method based on stochastic restart and obstacle avoidance improvement was proposed Gaussian Process Motion Planning with Stochastic Restart and Obstacle Avoidance Improvement (GPMP2-SROAI). Firstly,the random restart mechanism in the Covariant Hamiltonian Optimisation for Motion Planning (CHOMP) was introduced to apply perturbations to the trajectory to jump out of the local optimum and improve the efficiency and robustness of the trajectory optimization. Then,a Model Predictive Control with Control Barrier Function (MPC-CBF) approach based on the barrier function was introduced to avoid collisions by predicting the range of motion of the robot during the optimisation process. Simulation results show that, the improved algorithm achieves a success rate of 92.6 % in path planning,which is 24.9 % higher than that of GPMP2,12.5 % higher than the path shortest probability,and 4.8 % higher than the average smoothing degree,and also achieves a better quality of trajectory planning compared with the mainstream algorithms,with smoother trajectories and better obstacle avoidance effects.

Key words: path planning, random restart, robot obstacle avoidance, factor graph optimisation

中图分类号:

TP391.41

袁绪清, 魏媛媛, 王耀力, 常青, 付世沫. 基于随机重启的机器人高斯过程运动规划^*[J]. 现代制造工程, 2024, 531(12): 54-60.

YUAN Xuqing, WEI Yuanyuan, WANG Yaoli, CHANG Qing, FU Shimo. Gaussian process motion planning for robots based on random restarts[J]. Modern Manufacturing Engineering, 2024, 531(12): 54-60.

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基于随机重启的机器人高斯过程运动规划^*

Gaussian process motion planning for robots based on random restarts

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