Waverider: Leveraging Hierarchical, Multi-Resolution Maps for Efficient and Reactive Obstacle Avoidance

TL;DR

Waverider introduces a hierarchical, multi-resolution map-based reactive obstacle avoidance system that operates at high speed with minimal resources, enabling precise and robust navigation for mobile robots and micro aerial vehicles.

Contribution

The paper presents a novel hierarchical map representation and a parallelizable RMP-based obstacle avoidance algorithm that improves speed, efficiency, and versatility over existing methods.

Findings

Operates at hundreds of hertz with 36ms latency

Achieves large perceptive radius of 30m

Outperforms fixed-resolution RMP variants and CHOMP in evaluations

Abstract

Fast and reliable obstacle avoidance is an important task for mobile robots. In this work, we propose an efficient reactive system that provides high-quality obstacle avoidance while running at hundreds of hertz with minimal resource usage. Our approach combines wavemap, a hierarchical volumetric map representation, with a novel hierarchical and parallelizable obstacle avoidance algorithm formulated through Riemannian Motion Policies (RMP). Leveraging multi-resolution obstacle avoidance policies, the proposed navigation system facilitates precise, low-latency (36ms), and extremely efficient obstacle avoidance with a very large perceptive radius (30m). We perform extensive statistical evaluations on indoor and outdoor maps, verifying that the proposed system compares favorably to fixed-resolution RMP variants and CHOMP. Finally, the RMP formulation allows the seamless fusion of obstacle avoidance with additional objectives, such as goal-seeking, to obtain a fully-fledged navigation system that is versatile and robust. We deploy the system on a Micro Aerial Vehicle and show how it navigates through an indoor obstacle course. Our complete implementation, called waverider, is made available as open source.