Flow-Aided Flight Through Dynamic Clutters From Point To Motion

TL;DR

This paper introduces a LiDAR-based reinforcement learning approach for autonomous drone navigation through dynamic cluttered environments, emphasizing a change-aware sensing representation for real-time obstacle avoidance without explicit object tracking.

Contribution

It proposes a novel environment change sensing point flow integrated with depth maps, enabling efficient RL-based flight control in dynamic scenarios without object detection or prediction.

Findings

Achieves higher success rates in dynamic obstacle avoidance

Demonstrates real-world quadrotor deployment with safe maneuvers

Provides a lightweight, environment-aware sensing method

Abstract

Challenges in traversing dynamic clutters lie mainly in the efficient perception of the environmental dynamics and the generation of evasive behaviors considering obstacle movement. Previous solutions have made progress in explicitly modeling the dynamic obstacle motion for avoidance, but this key dependency of decision-making is time-consuming and unreliable in highly dynamic scenarios with occlusions. On the contrary, without introducing object detection, tracking, and prediction, we empower the reinforcement learning (RL) with single LiDAR sensing to realize an autonomous flight system directly from point to motion. For exteroception, a depth sensing distance map achieving fixed-shape, low-resolution, and detail-safe is encoded from raw point clouds, and an environment change sensing point flow is adopted as motion features extracted from multi-frame observations. These two are integrated into a lightweight and easy-to-learn representation of complex dynamic environments. For action generation, the behavior of avoiding dynamic threats in advance is implicitly driven by the proposed change-aware sensing representation, where the policy optimization is indicated by the relative motion modulated distance field. With the deployment-friendly sensing simulation and dynamics model-free acceleration control, the proposed system shows a superior success rate and adaptability to alternatives, and the policy derived from the simulator can drive a real-world quadrotor with safe maneuvers.