Autonomous navigation with convergence guarantees in complex dynamic environments
Dahlin, Albin and Karayiannidis, Yiannis LU
(2025)
In Automatica
173.
- Abstract
This article addresses the obstacle avoidance problem for setpoint stabilization tasks in complex dynamic 2-D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints through Model Predictive Control (MPC). The method is analytically proven to accomplish collision avoidance and convergence under soft conditions. Simulation scenarios using a non-holonomic unicycle robot is provided to showcase the efficacy of the control scheme.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c057345d-8593-4076-bfaf-b176d072f3e2
- author
- Dahlin, Albin
and Karayiannidis, Yiannis
LU
- organization
- publishing date
- 2025-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Autonomous systems, Control of constrained systems, Guidance navigation and control, Modeling for control optimization
- in
- Automatica
- volume
- 173
- article number
- 112026
- publisher
- Elsevier
- external identifiers
-
- scopus:85212153815
- ISSN
- 0005-1098
- DOI
- 10.1016/j.automatica.2024.112026
- project
- RobotLab LTH
- language
- English
- LU publication?
- yes
- id
- c057345d-8593-4076-bfaf-b176d072f3e2
- date added to LUP
- 2024-09-27 20:03:40
- date last changed
- 2025-04-04 15:08:43
@article{c057345d-8593-4076-bfaf-b176d072f3e2,
abstract = {{<p>This article addresses the obstacle avoidance problem for setpoint stabilization tasks in complex dynamic 2-D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints through Model Predictive Control (MPC). The method is analytically proven to accomplish collision avoidance and convergence under soft conditions. Simulation scenarios using a non-holonomic unicycle robot is provided to showcase the efficacy of the control scheme.</p>}},
author = {{Dahlin, Albin and Karayiannidis, Yiannis}},
issn = {{0005-1098}},
keywords = {{Autonomous systems; Control of constrained systems; Guidance navigation and control; Modeling for control optimization}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Automatica}},
title = {{Autonomous navigation with convergence guarantees in complex dynamic environments}},
url = {{http://dx.doi.org/10.1016/j.automatica.2024.112026}},
doi = {{10.1016/j.automatica.2024.112026}},
volume = {{173}},
year = {{2025}},
}