This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of parallel subtasks. Parallel subtask specifications are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. A dynamic Bayesian network (DBN) based human-robot trust model is constructed considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata.
- Dynamic Systems and Control Division
Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems
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Zheng, H, Liao, Z, & Wang, Y. "Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Atlanta, Georgia, USA. September 30–October 3, 2018. V003T30A010. ASME. https://doi.org/10.1115/DSCC2018-9161
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