A context-aware warehouse robots collision detection and avoidance using Markov Decision Process (MDP)

Yang Liu; Yu Sun

doi:10.1117/12.2639526

27 June 2022 A context-aware warehouse robots collision detection and avoidance using Markov Decision Process (MDP)

Yang Liu, Yu Sun

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122531M (2022) https://doi.org/10.1117/12.2639526
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Mobile robots are widely used in various fields, such as unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), unmanned underwater vehicles (UUV). It is because the single robot is only able to complete relatively simple tasks (while being helpless for complex and large-scale tasks) that a multi-robot system has emerged. The cooperation between multiple robots will not only promote the efficient completion of tasks and enhance the reliability of the system but advances cooperation to complete additional complex tasks. For a multi-robot system, the path planning and control of the robot is the basis for the successful completion of a particular task, which is simultaneously the key technology that needs to be solved. The application of warehousing robots can greatly improve the efficiency of e-commerce warehousing and logistics, as well as alleviating current situations of short supply in the warehousing and logistics industry. This article provides a multi-robot cooperative motion control method to solve the collision avoidance problem while the warehouse robot performs tasks.

Citation Download Citation

Yang Liu and Yu Sun "A context-aware warehouse robots collision detection and avoidance using Markov Decision Process (MDP)", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122531M (27 June 2022); https://doi.org/10.1117/12.2639526

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KEYWORDS

Robots

Collision avoidance

Computer programming

Control systems

Machine learning

Mobile robots

Motion controllers

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