Obstacle avoidance using Raycasting and Riemannian Motion Policies at kHz rates for MAVs
Abstract
This paper presents a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs), Today, most robotic obstacle avoidance algorithms rely on sampling or optimization-based planners with volumetric maps. However, they are computationally expensive and often have inflexible monolithic architectures. Riemannian Motion Policies are a modular, parallelizable, and efficient navigation alternative but are challenging to use with the widely used voxel-based environment representations. We propose using GPU raycasting and tens of thousands of concurrent policies to provide direct obstacle avoidance using Riemannian Motion Policies in voxelized maps without needing map smoothing or pre-processing. Additionally, we present how the same method can directly plan on LiDAR scans without any intermediate map. We show how this reactive approach compares favorably to traditional planning methods and can evaluate up to 200 million rays per second. We demonstrate the planner successfully on a real MAV for static and dynamic obstacles. The presented planner is made available as an open-source package. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000649334Publication status
publishedExternal links
Book title
2023 IEEE International Conference on Robotics and Automation (ICRA)Pages / Article No.
Publisher
IEEEEvent
Subject
Aerial robotics; Collision AvoidanceOrganisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Funding
205604 - NCCR Digital Fabrication (SNF)
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