Multi-Robot Symbolic Task and Motion Planning Leveraging Human Trust Models: Theory and Applications
Date of Award
11-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical Engineering
Committee Chair/Advisor
Yue Wang
Committee Member
John Wagner
Committee Member
Ardalan Vahidi
Committee Member
Yongqiang Wang
Abstract
Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies on formal verification to provide the provable-correct solution for the robotic system. One of the challenges in task and motion planning under temporal logic task specifications is developing computationally efficient MRS frameworks. The dissertation first presents an automaton-based task and motion planning framework for MRS to satisfy finite words of linear temporal logic (LTL) task specifications in parallel and concurrently. Furthermore, the dissertation develops a computational trust model to improve the human-MRS collaboration for a motion task. Notably, the current works commonly underemphasize the environmental attributes when investigating the impacting factors of human trust in robots. Our computational trust model builds a linear state-space (LSS) equation to capture the influence of environment attributes on human trust in an MRS. A Bayesian optimization based experimental design (BOED) is proposed to sequentially learn the human-MRS trust model parameters in a data-efficient way. Finally, the dissertation shapes a reward function for the human-MRS collaborated complex task by referring to the above LTL task specification and computational trust model. A Bayesian active reinforcement learning (RL) algorithm is used to concurrently learn the shaped reward function and explore the most trustworthy task and motion planning solution.
Recommended Citation
Zheng, Huanfei, "Multi-Robot Symbolic Task and Motion Planning Leveraging Human Trust Models: Theory and Applications" (2022). All Dissertations. 3217.
https://open.clemson.edu/all_dissertations/3217
Included in
Acoustics, Dynamics, and Controls Commons, Controls and Control Theory Commons, Dynamics and Dynamical Systems Commons, Navigation, Guidance, Control, and Dynamics Commons, Robotics Commons, Systems Engineering and Multidisciplinary Design Optimization Commons