Online Path Planning for Multi-Robot Multi-Source Seeking Using Distributed Gaussian Processes

Abstract

Multi-robot source seeking in unknown environments is challenging due to the difficulties in coordinating multi-robot sensing, information fusion and path planning. Existing approaches often struggle with computational scalability and search efficiency, particularly when dealing with multiple sources. In this paper, we develop a distributed multi-robot multi-source seeking strategy that enables robots to discover multiple sources using local sensing and neighbourhood communication. Our approach consists of three key components. First, we design a distributed mapping technique that leverages Gaussian processes for probabilistic inference across the entire environment and adapts it for a decentralised setup. Second, we formulate the source-seeking problem as an informative path planning problem and design a new information-theoretic objective function that combines predicted source locations with environmental uncertainty to prevent robots from being trapped at discovered sources. Third, we develop a tree search algorithm for planning the actions of robots over a fixed-horizon cycle. The algorithm generates a sequence of points leading to the most informative location. Based on the sequence, the robot is guided to the target location by taking a fixed-step movement inspired by the principles of model predictive control. Simulations validate our approach across different scenarios with varying numbers of sources and robots. In particular, the proposed information-theoretic heuristic outperforms the broadly used uncertainty-first and mean-gradient-first approaches, reducing search steps by up to 36.7%. Furthermore, our approach achieves an improvement of up to 63.8% in search efficiency compared to state-of-the-art coverage-based methods for multi-robot multi-source seeking problems. The average computational time of the proposed method is below 90 ms, supporting its feasibility for real-time applications.