Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for Multiple Wheeled Mobile Robots

Xiaoxiao Li, Zhihao Xu, Zerong Su, Hongpeng Wang*, Shuai Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

This paper proposes the distance- and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs' velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.

Original languageEnglish
Pages (from-to)1736-1748
JournalIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number2
DOIs
Publication statusPublished - 1 Feb 2024
MoE publication typeA1 Journal article-refereed

Funding

This work was supported in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005, in part by the Shenzhen Basic Research Project (Natural Science Foundation) under Grant JCYJ20210324132212030, and in part by the National Natural Science Foundation of China under Grant 62003102.

Keywords

  • Collision avoidance
  • multiple mobile robots
  • optimization
  • quadratic programming
  • trajectory tracking

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