How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

TL;DR

This paper introduces the Relative Needs Entropy (RNE) model to assess trust between robotic agents, enhancing cooperation and performance in multi-robot systems during complex urban search and rescue tasks.

Contribution

The paper presents a novel RNE trust model based on needs distribution, improving multi-robot cooperation over existing energy or distance-based methods.

Findings

RNE trust model improves robot grouping performance.

RNE enhances adaptability in heterogeneous robot teams.

Experimental results outperform traditional trust assessment models.

Abstract

Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations. Relationships between agents such as their spatial proximity and functional similarities could play a crucial role in cooperation between agents. Trust level between agents is an essential factor in evaluating their relationships' reliability and stability, much as people do. This paper proposes a new model called Relative Needs Entropy (RNE) to assess trust between robotic agents. RNE measures the distance of needs distribution between individual agents or groups of agents. To exemplify its utility, we implement and demonstrate our trust model through experiments simulating a heterogeneous multi-robot grouping task in a persistent urban search and rescue mission consisting of tasks at two levels of difficulty. The results suggest that RNE trust-Based grouping of robots can achieve better performance and adaptability for diverse task execution compared to the state-of-the-art energy-based or distance-based grouping models.