Empirical Study on Multi-Robot Task Allocation Under Communication Range Restrictions

Abstract

Efficient communication is vital for coordination in multi-robot systems and directly influences the overall performance of the system. However, many advanced approaches overlook the challenges of intermittent connectivity caused by communication range limitations. This paper addresses the assumption of global communication and instead assumes that robots have a predefined limited communication range, reflecting real-world conditions. This comparative empirical study evaluates the impact of limited communication range on three decentralized task allocation algorithms that typically assume continuous communication. Performance is analyzed by varying the communication model between—Bernoulli, Gilbert-Elliot, and Rayleigh Fading—and also the number of tasks in simulation. Each algorithm is assessed using four key metrics: (1) the total rejected tasks, (2) the cumulative penalty incurred by all robots, (3) the overall completion time, and (4) the cumulative energy consumption to execute all the accepted tasks. Finally, the study examines the impact of communication range constraints on the effectiveness of these approaches.