Università degli studi di Modena e Reggio Emilia

Multi-robot systems are gaining popularity over single-agent systems for their advantages. Although they have been studied in agriculture, search and rescue, surveillance, and environmental exploration, real-world implementation is limited due to agent coordination complexities caused by communication and sensor limitations. In this work, we propose a probabilistic approach to allow coordination among robots in communication-denied scenarios, where agents can only rely on visual information from a camera with a limited angular field-of-view. Our solution utilizes a particle filter to analyze uncertainty in the location of neighbors, together with Control Barrier Functions to address the exploration-exploitation dilemma that arises when robots must balance the mission goal with seeking information on undetected neighbors. This technique was tested with virtual robots required to complete a coverage mission, analyzing how the number of deployed robots affects performances and making a comparison with the ideal case of isotropic sensors and communication. Despite an increase in the amount of time required to fulfill the task, results have shown to be comparable to the ideal scenario in terms of final configuration achieved by the system.