Distributionally Robust RRT with Risk Allocation

Kajsa Ekenberg, Venkatraman Renganathan, Björn Olofsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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Abstract

An integration of distributionally robust risk allocation into sampling-based motion planning algorithms for robots operating in uncertain environments is proposed. We perform non-uniform risk allocation by decomposing the distributionally robust joint risk constraints defined over the entire planning horizon into individual risk constraints given the total risk budget. Specifically, the deterministic tightening defined using the individual risk constraints is leveraged to define our proposed exact risk allocation procedure. Embedding the risk allocation technique into sampling-based motion planning algorithms realises guaranteed conservative, yet increasingly more risk-feasible trajectories for efficient state-space exploration.
Original languageEnglish
Title of host publicationInternational Conference on Robotics and Automation (ICRA)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages12693-12699
Number of pages7
Publication statusPublished - 2023
Event 2023 IEEE International Conference on Robotics and Automation - United Kingdom, London
Duration: 2023 May 292023 Jun 2
https://www.icra2023.org/

Conference

Conference 2023 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA23
CityLondon
Period2023/05/292023/06/02
Internet address

Subject classification (UKÄ)

  • Control Engineering

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