Multi-Agent Systems for Search and Rescue Applications

Purpose of Review The goal of this review is to evaluate the current status of multi-robot systems in the context of search and rescue. This includes an investigation of their current use in the field, what major technical challenge areas currently preclude more widespread use, and which key topics will drive future development and adoption. Recent Findings Work blending machine learning with classical control techniques is driving progress in perception-driven autonomy, decentralized multi-robot coordination, and human–robot interaction, among others. Ad hoc mesh networking has achieved reliability suitable for safety-critical applications and may be a partial solution for communication. New modular and multimodal platforms may overcome mobility limitations without significantly increasing cost. Summary Multi-agent systems are not currently ready for deployment in search and rescue applications; however, progress is being made in a number of critical domains. As the field matures, research should focus on realistic evaluations of constituent technologies, and on confronting the challenges of simulation-to-reality transfer, algorithmic bias in autonomous agents that rely on machine learning, and novelty-versus-reliability incentive mismatch