Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response
This addresses the problem of efficient and robust task coordination in swarm robotics for disaster scenarios, offering a novel solution to a specific bottleneck.
The paper tackles decentralized multi-robotic task allocation for disaster response by developing a computationally efficient algorithm that handles task deadlines, robot constraints, and dynamic environments, achieving improved performance in a multi-UAV flood response simulation.
Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.