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

Abstract

Autonomous tiny drones are severely constrained by size, weight, power, and onboard computation, making reliable autonomous navigation particularly challenging. This work presents LiMPNet, a lightweight navigation framework that combines monocular vision, laser range sensing, and deep reinforcement learning to enable safe mapless navigation in cluttered environments. The system integrates a YOLOv8n-based obstacle detector, multi-ranger distance sensing, IMU-based state estimation, and a PPO navigation policy within a ROS and Gazebo simulation framework. Experimental results demonstrate reliable autonomous obstacle avoidance using only lightweight sensors, achieving a 100% success rate (112/112) in a simple environment and 35% success (7/20) in a densely cluttered environment. These results demonstrate that efficient autonomous navigation is feasible on highly resource-constrained aerial robots while maintaining low computational complexity.