A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots

Kong, Haiyi; Yang, Chenguang; Li, Guang; Dai, Shi-Lu

Abstract

We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collision avoidance guidance. The motion of the mobile robot is a resultant of compliant motion control and obstacle avoidance. In the mode of compliant motion, the sEMG signals obtained from the operator's forearms are transformed into human commands to control the moving direction and linear velocity of the mobile robot, respectively. When the mobile robot is blocked by obstacles, the motion mode is converted into obstacle avoidance. Aimed at the obstacle avoidance problem without a specific target, we develop a no-target Bug (NT-Bug) algorithm to guide the mobile robot to avoid obstacles and return to the command line. Besides, the command moving direction given by the operator is taken into consideration in the obstacle avoidance process to plan a smoother and safer path for the mobile robot. A model predictive controller is exploited to minimize the tracking errors. Experiments have been implemented to demonstrate the effectiveness of the proposed shared control strategy and the NT-Bug algorithm.

Citation

Kong, H., Yang, C., Li, G., & Dai, S. (2020). A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots. IEEE Access, 8, 26030-26040. https://doi.org/10.1109/access.2020.2970468

• Journal Article Type: Article
• Acceptance Date: Jan 17, 2020
• Online Publication Date: Jan 30, 2020
• Publication Date: Jan 30, 2020
• Deposit Date: Mar 29, 2020
• Publicly Available Date: Apr 1, 2020
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 26030-26040
• DOI: https://doi.org/10.1109/access.2020.2970468
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://uwe-repository.worktribe.com/output/5827647