Deep Reinforcement Learning Based Distributed Active Joint Localization and Target Tracking

In this paper, we investigate the problem of active joint localization and target tracking of mobile robots with onboard sensors. Our primary objective is concurrent target tracking while precisely localizing the robots through coordinated motion. A key constraint is the distributed setting, where each robot's observations are limited to its immediate vicinity, and communication is restricted to neighboring robots. To address this, we propose a novel reinforcement learning-based approach for active motion planning, grounded in a distributed estimation framework called Joint Localization and Target Tracking (JLATT). The policy is trained to optimize robot coordination and trajectories for enhanced self-localization, target tracking, and collision avoidance. Empirical analysis demonstrates our algorithm's effectiveness compared to benchmarks, both in collision avoidance and reducing estimation covariance, affirming its robustness for complex robotic systems.