Autonomous Flight through Cluttered Outdoor Environments Using a Memoryless Planner

TL;DR

This paper presents an enhanced collision avoidance system for multicopters in cluttered outdoor environments, extending the RAPPIDS planner to handle dynamic changes, sensor noise, and environment complexity, validated through forest flight experiments.

Contribution

It introduces solutions to improve the RAPPIDS planner's robustness and effectiveness for outdoor cluttered flight environments.

Findings

Successful outdoor forest flight experiments demonstrating system robustness.

Enhanced planner performance with dynamic adaptation and noise handling.

Low computational cost enabling real-time collision avoidance.

Abstract

This paper introduces a collision avoidance system for navigating a multicopter in cluttered outdoor environments based on the recent memory-less motion planner, rectangular pyramid partitioning using integrated depth sensors (RAPPIDS). The RAPPIDS motion planner generates collision-free flight trajectories at high speed with low computational cost using only the latest depth image. In this work we extend it to improve the performance of the planner by taking the following issues into account. (a) Changes in the dynamic characteristics of the multicopter that occur during flight, such as changes in motor input/output characteristics due to battery voltage drop. (b) The noise of the flight sensor, which can cause unwanted control input components. (c) Planner utility function which may not be suitable for the cluttered environment. Therefore, in this paper we introduce solutions to each of the above problems and propose a system for the successful operation of the RAPPIDS planner in an outdoor cluttered flight environment. At the end of the paper, we validate the proposed method's effectiveness by presenting the flight experiment results in a forest environment. A video can be found at www.youtube.com/channel/UCK-gErmvZlBODN5gQpNcpsg