LiMPNet: Lightweight Multi-sensor Perception and DRL Navigation for Tiny Drones in Mapless Environments

Abstract

Autonomous tiny drones face significant challenges in navigation due to strict constraints on size, weight, power, and onboard computational capacity. This paper presents a lightweight navigation framework that integrates basic multi-sensor perception with deep reinforcement learning (DRL) to enable safe, mapless flight in cluttered environments. We employ the Crazyflie 2.1 nano-drone, equipped with a grayscale camera and a multi-ranger deck, a laser-based distance sensor, for real-time obstacle detection and avoidance. A Proximal Policy Optimization (PPO) agent is trained within a ROS and Gazebo simulation environment to generate collision-free trajectories using fused visual and range data. The system is evaluated in two environments: a simple obstacle field, where the drone achieves a 100% success rate (112/112 episodes), and a densely cluttered map, where it reaches the target in 35% of trials (7/20). These results demonstrate that effective autonomous navigation is achievable using minimal sensing and low-computation models, making it well-suited for resource-constrained aerial platforms.