LSTP-Nav: Lightweight Spatiotemporal Policy for Map-Free Multi-Agent Navigation With LiDAR

Abstract

Safe map-free multi-robot navigation requires robots to make real-time decisions from partial and noisy local observations in dynamic, unstructured environments. Existing approaches often depend on prior maps, computationally intensive perception pipelines, or carefully tuned interaction models, which limit their robustness on resource-constrained platforms. This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net that maps stacked 2D LiDAR observations, goal information, and velocity feedback directly to action. LSTP-Net combines GRU-based temporal modeling with attention to extract interaction-critical spatiotemporal cues at low computational cost. We further introduce an HS reward to provide smooth, heading-aware safety feedback, and develop PhysReplay-SimLab to improve training effectiveness through local replay of near-failure interactions. Extensive simulation and real-world experiments show that LSTP-Nav outperforms representative baselines, achieving 98.6%–100.0% success in single-robot settings and 97.8%–99.0% success in 10-robot settings with 5–35 obstacles. The policy also remains robust to LiDAR degradation, nonconvex layouts, random seeds, and large-scale density tests, while achieving over 40 Hz onboard inference on a Raspberry Pi 3 Model B with zero-shot sim-to-real transfer. Overall, LSTP-Nav provides a practical, deployable solution for real-time, map-free multi-agent navigation on computationally constrained mobile robots. Note to Practitioners—This study addresses a pressing practical need in large-scale site inspection and outdoor search-and-rescue: enabling mobile robots with limited onboard computation to navigate safely and efficiently in complex, previously unseen environments without relying on pre-built maps. In real deployments, robots must make decisions from partial and noisy sensor observations while interacting with moving objects (e.g., pedestrians or teammates), and conventional map-based pipelines or computationally heavy planners can become unreliable or infeasible. To tackle these constraints, we propose a lightweight spatiotemporal navigation policy that directly converts raw LiDAR observations into motion commands, combining temporal memory with attention to focus computation on interaction-critical cues. In addition, a heading-stability objective and a physics-based replay-assisted training workflow improve behavior smoothness and reduce failure cases such as oscillation, collisions, and getting trapped in cluttered areas. Extensive simulation and real-world tests show that the proposed approach improves navigation success and safety over representative baselines while maintaining real-time performance on CPU-only platforms, making it suitable for practical deployment on resource-constrained robots in dynamic, unstructured environments.