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Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

Published online by Cambridge University Press:  28 October 2022

Wenbing Tang Open the ORCID record for Wenbing Tang [Opens in a new window] ,
Yuan Zhou Open the ORCID record for Yuan Zhou [Opens in a new window] ,
Tianwei Zhang ,
Yang Liu ,
Jing Liu  and
Zuohua Ding
Wenbing Tang
Affiliation:
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China
Yuan Zhou*
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Tianwei Zhang
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Yang Liu
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Jing Liu
Affiliation:
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China
Zuohua Ding
Affiliation:
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
*
*Corresponding author. E-mail: y.zhou@ntu.edu.sg
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Abstract

Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

Keywords

collision avoidancefuzzy rulesmultirobot systemsvelocity obstacles
Type
Research Article
Information
Robotica , Volume 41 , Issue 2 , February 2023 , pp. 668 - 689
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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