Obstacle Avoidance in Dynamic Environments via Tunnel-following MPC with Adaptive Guiding Vector Fields
Dahlin, Albin and Karayiannidis, Yiannis LU
(2023)
2023 62nd IEEE Conference on Decision and Control (CDC)
p.5778-5783
- Abstract
- This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obstacles. The effectiveness of the control scheme is illustrated by means of simulation.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6efd36d8-366b-4b6b-9da7-576db57e0ea7
- author
- Dahlin, Albin
and Karayiannidis, Yiannis
LU
- organization
- publishing date
- 2023-12-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the IEEE Conference on Decision and Control
- pages
- 6 pages
- conference name
- 2023 62nd IEEE Conference on Decision and Control (CDC)
- conference location
- Marina Bay Sands, Singapore
- conference dates
- 2023-12-13 - 2023-12-15
- external identifiers
-
- scopus:85184823675
- ISBN
- 979-835030124-3
- DOI
- 10.1109/CDC49753.2023.10383988
- project
- ELLIIT B14: Autonomous Force-Aware Swift Motion Control
- language
- English
- LU publication?
- yes
- id
- 6efd36d8-366b-4b6b-9da7-576db57e0ea7
- date added to LUP
- 2023-12-22 14:50:06
- date last changed
- 2024-02-27 10:44:46
@inproceedings{6efd36d8-366b-4b6b-9da7-576db57e0ea7,
abstract = {{This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obstacles. The effectiveness of the control scheme is illustrated by means of simulation.}},
author = {{Dahlin, Albin and Karayiannidis, Yiannis}},
booktitle = {{Proceedings of the IEEE Conference on Decision and Control}},
isbn = {{979-835030124-3}},
language = {{eng}},
month = {{12}},
pages = {{5778--5783}},
title = {{Obstacle Avoidance in Dynamic Environments via Tunnel-following MPC with Adaptive Guiding Vector Fields}},
url = {{http://dx.doi.org/10.1109/CDC49753.2023.10383988}},
doi = {{10.1109/CDC49753.2023.10383988}},
year = {{2023}},
}