Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems

TL;DR

This paper introduces a framework integrating human-robot trust into task allocation and motion planning for multi-robot systems, enhancing collaboration by dynamically considering trust levels during task execution.

Contribution

It develops a novel trust-aware task allocation automaton using a dynamic Bayesian network, enabling adaptive planning based on human trust in robots.

Findings

Trust-based task allocation improves system efficiency.

Dynamic trust modeling enhances robot performance and safety.

Simulation demonstrates effective trust integration in multi-robot coordination.

Abstract

This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel 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. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by 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.