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Article

Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

1
Department of Mechatronics Engineering, Atilim University, Ankara 06830, Turkey
2
Defense Technologies Institute, Gebze Technical University, Kocaeli 41400, Turkey
3
Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7905; https://doi.org/10.3390/app10217905
Received: 30 September 2020 / Revised: 29 October 2020 / Accepted: 4 November 2020 / Published: 7 November 2020
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck. View Full-Text
Keywords: mobile robots; path planning; bacterial foraging optimization; local minima; information sharing; swarm robots; dynamic environment; static environment mobile robots; path planning; bacterial foraging optimization; local minima; information sharing; swarm robots; dynamic environment; static environment
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MDPI and ACS Style

Abdi, M.I.I.; Khan, M.U.; Güneş, A.; Mishra, D. Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm. Appl. Sci. 2020, 10, 7905. https://doi.org/10.3390/app10217905

AMA Style

Abdi MII, Khan MU, Güneş A, Mishra D. Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm. Applied Sciences. 2020; 10(21):7905. https://doi.org/10.3390/app10217905

Chicago/Turabian Style

Abdi, Mohammed I.I., Muhammad U. Khan, Ahmet Güneş, and Deepti Mishra. 2020. "Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm" Applied Sciences 10, no. 21: 7905. https://doi.org/10.3390/app10217905

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