Real-Time Path Planning of Velocity Potential for Robot in Grid Map Environment

doi:10.3778/j.issn.1002-8331.2103-0094

Abstract

Abstract:

Aiming at the problem of automatic obstacle avoidance of mobile robot in intelligent manufacturing engineering environment, a real-time obstacle avoidance path planning method based on grid map is proposed. The grid method is used to binarize the working scene of the mobile robot, and the grid search is carried out from the center of the robot to different directions. Based on the idea that the obstacles repel the mobile robot and the target points attract the robot, the real-time distance between the robot and the obstacles is calculated by the accumulation of the number of grids, and the negative velocity increment function is established by taking the shape of the obstacles and the minimum safe distance into account. The increment function of forward velocity is established with the parameters of velocity and angle. Then, based on the robot kinematics model, the velocity potential function is defined to drive the mobile robot in real time. By setting the minimum velocity increment, the problem of local minimum at the zero potential point is avoided. By setting the distance threshold, the problem of infinite velocity increment near the target point is avoided. Finally, the proposed algorithm is verified by simulation.

Key words: mobile robot, artificial potential field, velocity potential, obstacle avoidance, real-time path planning

摘要：

针对智能制造工程环境中移动机器人的自动避障问题，提出一种基于栅格地图的移动机器人速度势实时避障路径规划方法。利用栅格法二值化移动机器人的工作场景，从机器人中心出发向不同方向进行栅格搜索。基于障碍物对移动机器人有排斥作用以及目标点对机器人有吸引作用的思想，通过栅格数的累加计算机器人到障碍物之间的实时距离，并以此为参数，考虑障碍物的形状、最小安全距离等因素的影响来建立负的速度增量函数；以机器人当前位置与目标点的实时距离和角度为参数建立正向速度增量函数。进而在机器人运动学模型基础上，定义速度势函数来对移动机器人进行实时速度驱动。通过设置最小速度增量，避免在零势点处的局部极小点问题；通过设立距离阈值，避免在目标点附近速度增量趋于无穷的问题。通过仿真对所提出的算法进行验证。

关键词: 移动机器人, 人工势场, 速度势, 避障, 实时路径规划

YANG Lingyao, ZHANG Aihua, ZHANG Jie, SONG Jiqiang. Real-Time Path Planning of Velocity Potential for Robot in Grid Map Environment[J]. Computer Engineering and Applications, 2021, 57(24): 290-295.

杨凌耀，张爱华，张洁，宋季强. 栅格地图环境下机器人速度势实时路径规划[J]. 计算机工程与应用, 2021, 57(24): 290-295.

References

[1] MATOUI F，BOUSSAID B，ABDELKRIM M N.Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach[J].Simulation，2019，95（7）：637-657.
[2] 郭银景，孟庆良，孔芳，等.AUV路径规划算法研究现状与展望[J].计算机科学与探索，2020，14（12）：1981-1994.
GUO Y J，MENG Q L，KONG F，et al.Research status and prospect of AUV path planning algorithms[J].Journal of Frontiers of Computer Science and Technology，2020，14（12）：1981-1994.
[3] 方玉发.基于麦克纳姆轮的重载AGV关键技术研究与应用[D].杭州：浙江大学，2019.
FANG Y F.Research and application of key technologies of heavy load AGV with mecanum wheel[D].Hangzhou：Zhejiang University，2019.
[4] 余文凯，章政，付雪画，等.基于地图预处理及改进A~*算法的路径规划[J].高技术通讯，2020，30（4）：383-390.
YU W K，ZHANG Z，FU X H，et al.Path planning based on map preprocessing and improved A~* algorithm[J].High Technology Letters，2020，30（4）：383-390.
[5] WANG P，GAO S，LI L，et al.Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm[J].Energies，2019，12（12）：220-225.
[6] DAI X L，LONG S，ZHANG Z W，et al.Mobile robot path planning based on ant colony algorithm with A* heuristic method[J].Frontiers in Neurorobotics，2019，13：15.
[7] BACK S，CHO G，OH J，et al.Autonomous UAV trail navigation with obstacle avoidance using deep neural networks[J].Journal of Intelligent and Robotic Systems，2020，100（2）：1-17.
[8] 张献，任耀峰，王润芃.基于自适应遗传算法的连续时空最优搜索路径规划研究[J].兵工学报，2015，36（12）：2386-2395.
ZHANG X，REN Y F，WANG R P.Research on continuous space time optimal search path planning based on adaptive genetical algorithm[J].Acta Armamentarri，2015，36（12）：2386-2395.
[9] KHATIB O.Real-time obstacle avoidance for manipulators and mobile robots[J].International Journal of Robotics Research，1986，5（1）：90-98.
[10] 程志，张志安，李金芝，等.改进人工势场法的移动机器人路径规划[J].计算机工程与应用，2019，55（23）：29-34.
CHEN Z，ZHANG Z A，LI J Z，et al.Mobile robots path planning based on improved artificial potential field[J].Computer Engineering and Applications，2019，55（23）：29-34.
[11] 马小陆，梅宏.基于改进势场蚁群算法的移动机器人全局路径规划[J].机械工程学报，2021，57（1）：19-27.
MA X L，MEI H.Mobile robot global path planning based on improved ant colony system algorithm with potential field[J].Journal of Mechanical Engineering，2021，57（1）：19-27.
[12] 张祺，杨宜民.基于改进人工势场法的足球机器人避碰控制[J].机器人，2002（1）：12-15.
ZAHNG Q，YANG Y M.Soccer robot collision avoidance control based on evolutionary artificial potential field[J].Robot，2002（1）：12-15.
[13] 王肖青，王奇志.传统人工势场的改进[J].计算机技术与发展，2006（4）：96-98.
WANG X Q，WANG Q Z.Improvement of traditional artificial potential field[J].Computer Technology and Development，2006（4）：96-98.
[14] 马杰，苏钰栋，熊勇，等.基于速度障碍和人工势场的受限水域船舶避碰决策方法[J].中国安全科学学报，2020，30（11）：60-66.
MA J，SU Y D，XIONG Y，et al.Decision-making method for collision avoidance of ships in confined waters based on velocity obstacle and artificial potential field[J].China Safety Science Journal，2020，30（11）：60-66.
[15] LAZAROWSKA A.Discrete artificial potential field approach to mobile robot path planning[J].IFAC-PapersOnLine，2019，52（8）：277-282.
[16] AZZABI A，NOURI K.An advanced potential field method proposed for mobile robot path[J].Transactions of the Institute of Measurement and Control，2019，41（11）：3132-3144.
[17] ZHANG N，ZHANG Y，MA C，et al.Path planning of six-DOF serial robots based on improved artificial potential field method[C]//2017 IEEE International Conference on Robotics and Biomimetics（ROBIO）.Macau，China：IEEE，2017：617-621.