A Gossip-Based Approach for Measurement Task Allocation and Routing in Multi-Robot Systems with Heterogeneous Sensing

This paper presents a decentralized task allocation strategy for heterogeneous multi-robot systems to minimize makespan during mission execution. The approach leverages a Gossip-based consensus mechanism, where robots communicate and exchange task information to optimize task distribution. The problem is modelled as a Multi-Robot Task Allocation (MRTA) challenge with the objective of minimizing task completion time (makespan). The proposed heuristic algorithm operates by iteratively improving task sequences via local exchanges between robots. Simulations demonstrate the algorithm's effectiveness in assigning tasks while considering various robot capabilities and environmental constraints, resulting in improved mission performance and reduced overall task completion time.