Distributionally Robust RRT with Risk Allocation
(2023) 2023 IEEE International Conference on Robotics and Automation p.12693-12699- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e67144b4-bede-457d-8437-3e4874b101da
- author
- Ekenberg, Kajsa ; Renganathan, Venkatraman LU and Olofsson, Björn LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- International Conference on Robotics and Automation (ICRA)
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2023 IEEE International Conference on Robotics and Automation
- conference location
- London
- conference dates
- 2023-05-29 - 2023-06-02
- external identifiers
-
- scopus:85168668470
- project
- RobotLab LTH
- Scalable Control of Interconnected Systems
- language
- English
- LU publication?
- yes
- id
- e67144b4-bede-457d-8437-3e4874b101da
- alternative location
- https://arxiv.org/pdf/2209.08391.pdf
- date added to LUP
- 2023-04-06 13:35:03
- date last changed
- 2023-11-21 04:02:22
@inproceedings{e67144b4-bede-457d-8437-3e4874b101da,
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.}},
author = {{Ekenberg, Kajsa and Renganathan, Venkatraman and Olofsson, Björn}},
booktitle = {{International Conference on Robotics and Automation (ICRA)}},
language = {{eng}},
pages = {{12693--12699}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Distributionally Robust RRT with Risk Allocation}},
url = {{https://arxiv.org/pdf/2209.08391.pdf}},
year = {{2023}},
}