- Title
- Machine learning-based agoraphilic navigation algorithm for use in dynamic environments with a moving goal
- Creator
- Hewawasam, Hasitha; Kahandawa, Gayan; Ibrahim, Yousef
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194242
- Identifier
- vital:18327
- Identifier
-
https://doi.org/10.3390/machines11050513
- Identifier
- ISSN:2075-1702
- Abstract
- This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
- Publisher
- MDPI AG
- Relation
- Machines Vol. 11, no. 5 (2023), p. 513
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
- Rights
- Open Access
- Subject
- Agoraphilic; Algorithms; Attraction; Control systems; Control systems design; Data processing; Dynamic environment; Estimates; Free-space; Kalman filters; Machine learning; Mathematical programming; Methods; Mobile robots; Moving goal; Moving obstacles; Navigation; Robots; Velocity; 4003 Biomedical engineering
- Full Text
- Reviewed
- Funder
- This research was supported by Australian Government Research Training Program (RTP) via Federation University of Australia.
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