Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions
2026-06-01 • Robotics
RoboticsMultiagent Systems
AI summaryⓘ
The authors present a new way for groups of drones to work together during search and rescue missions, even if some drones stop working. They created a system called IRDS that lets drones decide who should cover which area by bidding based on how far they are and share information to confirm targets. In tests, the drone group quickly adapted when some drones failed and still completed the mission successfully most of the time. This shows their system helps drone teams keep working smoothly despite problems.
UAV swarmSearch and Rescue (SAR)fault tolerancedistributed coordinationreverse auctioncost functiongeometric consensusstochastic fault injectionself-healing roboticsresource-constrained environments
Authors
Luiz Giacomossi, Andrea Haglund, Claire Namatovu, Emily Zainali, Esaias Målqvist, Yonatan M. Beyene, Ivan Tomasic, Baran Çürüklü, Håkan Forsberg
Abstract
Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent degradation. This paper introduces the Intelligent Replanning Drone Swarm (IRDS), a distributed coordination architecture designed for resource-constrained environments. The proposed framework employs a Reverse-Auction market mechanism where agents bid to service search sectors based on a distance-weighted cost function, coupled with a geometric consensus protocol for target verification. We evaluate the approach through physics-based simulations (N=8 agents, 8x8 grid) subjected to stochastic fault injection. Results indicate that the swarm autonomously reallocates tasks from failed agents with low latency relative to the total mission duration, maintaining a mission success rate of 93% under 25% workforce degradation. The proposed framework demonstrates a robust, empirically tested method for self-healing aerial robotic coordination.