Wenke Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Wenke Ma has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Haojie Chen
College of Sino-German Intelligent Manufacturing, Shenzhen Technology University, 3002 Lantian Road, Shenzhen 518118, China
1 shared publication - Qiang Wang
Shenzhen Research Institute of Northwestern Polytechnical University, 45 Gaoxin South 9th Road, Shenzhen 518057, China
1 shared publication - Tan Zhang
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
1 shared publication - Wenjun J. Zhang
Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, SK, Canada
1 shared publication - Xiaoxu Liu
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
1 shared publication - Zifan Wang
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, 3002 Lantian Road, Shenzhen 518000, China
1 shared publication
Latest Publications
- Open Access
A Hybrid Motion Planning Algorithm for Multi-Mobile Robot Formation Planning
This paper addresses the problem of relative position-based formation planning for a leader–follower multi-robot setup, where the robots adjust the formation parameters, such as size and three-dimensional orientation, to avoid collisions and progress toward their goal. Specifically, we develop a virtual sub-target-based obstacle avoidance method, which involves a transitional virtual sub-target that guides the robots to avoid obstacles according to obstacle information, target, and boundary. Moreover, we develop a changing formation strategy to determine the necessity to avoid collisions and a priority-based model to determine which robots move, thus dynamically adjusting the relative distance between the followers and the leader. The backstepping-based sliding motion controller guarantees that the trajectory and velocity tracking errors converge to zero. The proposed robot navigation method can be employed in various environments and types of obstacles, allowing for a formation change. Furthermore, it is efficient and scalable under various numbers of robots. The approach is experimentally verified using three real mobile robots and up to five mobile robots in simulated scenarios.
Open AccessA Hybrid Motion Planning Algorithm for Multi-Mobile Robot Formation Planning
This paper addresses the problem of relative position-based formation planning for a leader–follower multi-robot setup, where the robots adjust the formation parameters, such as size and three-dimensional orientation, to avoid collisions and progress toward their goal. Specifically, we develop a virtual sub-target-based obstacle avoidance method, which involves a transitional virtual sub-target that guides the robots to avoid obstacles according to obstacle information, target, and boundary. Moreover, we develop a changing formation strategy to determine the necessity to avoid collisions and a priority-based model to determine which robots move, thus dynamically adjusting the relative distance between the followers and the leader. The backstepping-based sliding motion controller guarantees that the trajectory and velocity tracking errors converge to zero. The proposed robot navigation method can be employed in various environments and types of obstacles, allowing for a formation change. Furthermore, it is efficient and scalable under various numbers of robots. The approach is experimentally verified using three real mobile robots and up to five mobile robots in simulated scenarios.