  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.
