Improving UAV Obstacle Avoidance by Environment Dependent Cost for Path Planning

Andreas Thoma, Carsten Braun, Alex Fisher, Alessandro Gardi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Most local path planning algorithms rely on several cost functions representing different aspects of the planned path, e.g., path smoothness and deviation from goal, to evaluate alternative flight paths and define the next waypoint(s). The cost functions are weighed against each other by fixed or user predefined weighting parameters. Previous investigations showed that an optimal choice of weights differs significantly between various environments. This work presents a method to automatically choose weighting parameters for flight velocity, minimum obstacle distance, yaw, and pitch cost depending on the environment. The presented strategies are formulated such that they fit various path planning algorithms. The performance of the proposed method is tested with the 3DVFH* in the px4-Avoidance implementation. The adaptions show significant improvement in failure probability for environments with small to medium sized, rectangular obstacles and environments containing different kinds of forests. The failure probability of city-like environments is very sensitive to the correct implementation of the avoidance strategies. However, a proper choice leads to a reduction of failure probability as well.

Original languageBritish English
Title of host publicationAIAA SciTech Forum 2022
DOIs
StatePublished - 2022
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 - San Diego, United States
Duration: 3 Jan 20227 Jan 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Country/TerritoryUnited States
CitySan Diego
Period3/01/227/01/22

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