DAPTP: Distributed Awareness Planner using Time Potential for dynamic target search

Abstract

This paper tackles the challenge of using multiple robots to search for unknown dynamic targets in complex, large environments. As both the number of robots and environmental complexity increase, coordinating efficient distributed searches becomes more difficult. Previous methods define search as either exploration or utilizing an initial target distribution to accelerate the process. However, these methods fail to dynamically build or update the target distribution from scratch and overlook the differences between the already searched regions during the search. We propose DAPTP, a novel Distributed Awareness Planner using Time Potential for dynamic target search. At the core of DAPTP is the concept of the time potential map, which estimates the target distribution in the environment based on historical search information. The importance of the searched regions is distinguished based on their corresponding time potential. Building on this, the coverage and search direction for each robot is then planned by maximizing the change in time potential, ensuring that areas with the greatest potential variation receive prioritized attention. We conduct extensive experiments both in simulations and real-world scenarios. The results demonstrate that our approach significantly surpasses state-of-the-art methods in terms of reducing search steps and improving collective environmental awareness, area search rate, detected target number, and success rate. The source code is available at: https://github.com/arclab-hku/DAPTP . • We propose a map to capture past search information and reflect target distribution. • We propose DAPTP to cover areas and revisit zones for dynamic target search. • A novel metric leverages time potential averages to assess multi-robot awareness. • We validated our approach via extensive simulations and real-world target detection.