DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy - Yusuf, Sagir M.; Baber, Chris (Frontiers in Robotics and AI, 2022)
This paper describes an approach for multiagent search planning for a team of agents. A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains. Fixed-path (e.g., parallel track) methods for multiagent search can produce predictable and structured paths with the main limitation being poor management of agents’ resources and limited adaptability(i.e., based on predefined geometric paths, e.g., parallel track, expanding square, etc.). On the other hand, pseudorandom methods allow agents to generate a well-separated paths; but methods can be computationally expensive and can result in lack of coordination of agents’ activities. We present a hybrid solution that exploits the complementary strengths of fixed-pattern and pseudorandom methods, i.e., an approach that is resource-efficient, predictable, adaptable, and scalable. Our approach evolved from the Delaunay triangulation of systematically selected waypoints to allocate agents to explore a specific region while optimizing a given set of mission constraints. We implement our approach in a simulation environment, comparing performance of the proposed algorithm with fixed-path and pseudorandom baselines. Results proved agents' resources utilization, predictability, scalability, and adaptability of the developed path. We also demonstrate the proposed algorithm’s application on real UAVs.