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Cooperation in multi-agent and multi-robot systems can help agents buildvarious formations, shapes, and patterns presenting corresponding functions andpurposes adapting to different situations. Relationships between agents such astheir spatial proximity and functional similarities could play a crucial rolein cooperation between agents. Trust level between agents is an essentialfactor in evaluating their relationships' reliability and stability, much aspeople do. This paper proposes a new model called Relative Needs Entropy (RNE)to assess trust between robotic agents. RNE measures the distance of needsdistribution between individual agents or groups of agents. To exemplify itsutility, we implement and demonstrate our trust model through experimentssimulating a heterogeneous multi-robot grouping task in a persistent urbansearch and rescue mission consisting of tasks at two levels of difficulty. Theresults suggest that RNE trust-Based grouping of robots can achieve betterperformance and adaptability for diverse task execution compared to thestate-of-the-art energy-based or distance-based grouping models.

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