Velocity Obstacle Based on Vertical Ellipse for Multi-Robot Collision Avoidance

被引:14
|
作者
Zhu, Xiaomin [1 ]
Yi, Jianjun [1 ]
Ding, Hongkai [1 ]
He, Liang [2 ]
机构
[1] East China Univ Sci & Technol, Sch Mech & Power Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China
[2] Shanghai Aerosp Control Technol Inst, Shanghai Key Lab Aerosp Intelligent Control Techn, 1555 Zhongchun Rd, Shanghai 201100, Peoples R China
基金
中国国家自然科学基金;
关键词
Velocity obstacle (VO); Vertical ellipse; Multi-robot; Path planning; Localization uncertainty;
D O I
10.1007/s10846-019-01127-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bounding volume based approaches in velocity obstacle (VO) provide a good solution for collision avoidance of mobile robots with uncertainty. However, the VO built with the bounding footprint always has over-constraining problems which may lead to conservative maneuvers of the mobile robots. Addressing this problem, a vertical ellipse based velocity obstacle (VEVO) collision avoidance method is proposed in this paper. The method mitigates the over-constraining situation by building the footprint probability ellipse whose minor axis is vertical to the direction of the obstacle to minimize the VO area. Based on VEVO, a DWA (Dynamic Window Approach) integrated method is proposed to provide a set of available velocities in speed selection. According to different collision avoidance objectives like collision safety, shortest time consumption and shortest trajectory length, a multi-objective velocity selecting strategy is proposed to provide optimal velocities for motion planning. Furthermore, a dynamic local path adjustment method is proposed to help robots react to the closest obstacle (dynamic or static) according to different collision safety requirements. We validate our methods in a simulated workspace with different numbers of robots going to their goal points. Experimental results show VEVO method could improve the collision avoidance performance in crowded multi-robot environment and robots could achieve their different objectives when suitable parameters are set in the velocity evaluation function. The proposed dynamic local path adjustment method only affects the trajectories in local areas and could ensure collision avoidance safety and performance at the same time.
引用
收藏
页码:183 / 208
页数:26
相关论文
共 50 条
  • [31] A Switching Formation Strategy for Obstacle Avoidance of Multi-Robot System
    Dai, Yanyan
    Lee, SukGyu
    Kim, Yoon-Gu
    Wee, Sung-Gil
    2014 IEEE 4TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2014, : 457 - 462
  • [32] Multi-Robot Source Location of Scalar Fields by a Novel Swarm Search Mechanism With Collision/Obstacle Avoidance
    Li, Rui-Guo
    Wu, Huai-Ning
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 249 - 264
  • [33] Formation and Obstacle Avoidance in the Unknown Environment of Multi-Robot System
    Zhang, Tao
    Li, Xiaqin
    Zhu, Yi
    Chen, Song
    Cheng, Yu
    Song, Jingyan
    2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO 2009), VOLS 1-4, 2009, : 729 - +
  • [34] Formation Control Design for Multi-Robot System with Obstacle Avoidance
    Huang Jie
    Dou Lihua
    Fang Hao
    Wei Yue
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 8547 - 8552
  • [35] Embedding Obstacle Avoidance in the Control of a Flexible Multi-Robot Formation
    Rampinelli, V. T. L.
    Brandao, A. S.
    Sarcinelli-Filho, M.
    Martins, F. N.
    Carelli, R.
    IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE 2010), 2010, : 1846 - 1851
  • [36] Collision avoidance in multi-robot systems based on multi-layered reinforcement learning
    Arai, Y
    Fujii, T
    Asama, H
    Kaetsu, H
    Endo, I
    ROBOTICS AND AUTONOMOUS SYSTEMS, 1999, 29 (01) : 21 - 32
  • [37] Velocity Obstacle Approaches for Multi-Agent Collision Avoidance
    Douthwaite, James A.
    Zhao, Shiyu
    Mihaylova, Lyudmila S.
    UNMANNED SYSTEMS, 2019, 7 (01) : 55 - 64
  • [38] A laser-based multi-robot collision avoidance approach in unknown environments
    Yu, Yingying
    Wu, Zhiyong
    Cao, Zhiqiang
    Pang, Lei
    Ren, Liang
    Zhou, Chao
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2018, 15 (01):
  • [39] Multi-Robot Collision Avoidance with Map-based Deep Reinforcement Learning
    Yao, Shunyi
    Chen, Guangda
    Pan, Lifan
    Ma, Jun
    Ji, Jianmin
    Chen, Xiaoping
    2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI), 2020, : 532 - 539
  • [40] Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments
    Chen, Yimei
    Wang, Yixin
    Li, Baoquan
    Kamiya, Tohru
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2024, 51 (04): : 607 - 616