LSTP-Nav: Lightweight Spatiotemporal Policy for Map-free Multi-agent Navigation with LiDAR

Abstract

Safe and efficient multi-agent navigation in dynamic environments remains inherently challenging, particularly when real-time decision-making is required on resource-constrained platforms. Ensuring collision-free trajectories while adapting to uncertainties without relying on pre-built maps further complicates real-world deployment. To address these challenges, we propose LSTP-Nav, a lightweight end-to-end policy for multi-agent navigation that enables map-free collision avoidance in complex environments by directly mapping raw LiDAR point clouds to motion commands. At the core of this framework lies LSTP-Net, an efficient network that processes raw LiDAR data using a GRU architecture, enhanced with attention mechanisms to dynamically focus on critical environmental features while minimizing computational overhead. Additionally, a novel HS reward optimizes collision avoidance by incorporating angular velocity, prioritizing obstacles along the predicted heading, and enhancing training stability. To narrow the sim-to-real gap, we develop PhysReplay-Simlab, a physics-realistic multi-agent simulator, employs localized replay to mine near-failure experiences. Relying solely on LiDA, LSTP-Nav achieves efficient zero-shot sim-to-real transfer on a CPU-only robotic platform, enabling robust navigation in dynamic environments while maintaining computation frequencies above 40 Hz. Extensive experiments demonstrate that LSTP-Nav outperforms baselines with a 9.58% higher success rate and a 12.30% lower collision rate, underscoring its practicality and robustness for real-world applications.