Distributed Multi-Robot Multi-Target Simultaneous Search and Tracking in an Unknown Non-convex Environment

2025-09-27

AI Summary

In unknown non-convex environments (e.g., indoor or underground spaces), simultaneous multi-robot exploration, search, and target tracking remain challenging due to conflicting objectives and lack of centralized infrastructure. Method: This paper proposes a distributed motion planning framework that unifies frontier-based exploration, Lloyd-type coverage optimization, and sensor-driven multi-target tracking within a single cohesive model. It employs an adaptive frontier selection strategy for efficient environmental coverage, enhances Lloyd’s algorithm for improved coverage uniformity, and integrates real-time sensor feedback for high-accuracy active target tracking—all executed in a fully decentralized manner. Contribution/Results: MATLAB simulations demonstrate that the proposed approach significantly outperforms state-of-the-art methods in key metrics: coverage ratio, target localization error, and response latency. It achieves a principled trade-off between exploration efficiency and data acquisition accuracy in complex non-convex settings, without reliance on central coordination.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

In unknown non-convex environments, such as indoor and underground spaces, deploying a fleet of robots to explore the surroundings while simultaneously searching for and tracking targets of interest to maintain high-precision data collection represents a fundamental challenge that urgently requires resolution in applications such as environmental monitoring and rescue operations. Current research has made significant progress in addressing environmental exploration, information search, and target tracking problems, but has yet to establish a framework for simultaneously optimizing these tasks in complex environments. In this paper, we propose a novel motion planning algorithm framework that integrates three control strategies: a frontier-based exploration strategy, a guaranteed coverage strategy based on Lloyd's algorithm, and a sensor-based multi-target tracking strategy. By incorporating these three strategies, the proposed algorithm balances coverage search and high-precision active tracking during exploration. Our approach is validated through a series of MATLAB simulations, demonstrating validity and superiority over standard approaches.

Problem

• Simultaneous search and tracking in unknown non-convex environments
• Balancing coverage exploration with high-precision target tracking
• Integrating multi-robot control strategies for complex environment operations

Innovation

• Frontier-based exploration strategy for environment mapping
• Lloyd's algorithm for guaranteed coverage search
• Sensor-based multi-target tracking for precision monitoring