Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA)
Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Recipr...
Published in: | Neural Computing and Applications |
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ftkentuniv:oai:kar.kent.ac.uk:106137 2024-06-23T07:55:57+00:00 Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) Romlay, Muhammad Ibrahim, Azhar Mohd Toha, Siti Fauziah De Wilde, Philippe Venkat, Ibrahim Ahmad, Muhammad 2023-08-10 application/pdf application/vnd.openxmlformats-officedocument.wordprocessingml.document https://kar.kent.ac.uk/106137/ https://kar.kent.ac.uk/106137/13/Research%20Article%20%5BFLC-ORCA%5D.pdf https://kar.kent.ac.uk/106137/11/Research%20Article%20%5BFLC-ORCA%5D.docx https://doi.org/10.1007/s00521-023-08856-8 en eng Springer https://kar.kent.ac.uk/106137/13/Research%20Article%20%5BFLC-ORCA%5D.pdf https://kar.kent.ac.uk/106137/11/Research%20Article%20%5BFLC-ORCA%5D.docx Romlay, Muhammad, Ibrahim, Azhar Mohd, Toha, Siti Fauziah, De Wilde, Philippe, Venkat, Ibrahim, Ahmad, Muhammad (2023) Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Neural Computing and Applications, 35 . pp. 22405-22429. ISSN 0941-0643. (doi:10.1007/s00521-023-08856-8 <https://doi.org/10.1007/s00521-023-08856-8>) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:106137 </106137>) T Technology Article PeerReviewed 2023 ftkentuniv https://doi.org/10.1007/s00521-023-08856-8 2024-06-11T23:46:56Z Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Our FLC-ORCA method assigns responsibility for collision avoidance and predicts the velocity of obstacles using a LiDAR-based mobile robot. We conduct experiments in the presence of static, dynamic, and intelligent entities, recording navigation paths, time taken, angle changes, and rerouting occurrences. The results demonstrate that the proposed FLC-ORCA successfully avoids collisions among objects with different collision avoidance protocols and varying liabilities in circumventing obstacles. Comparative analysis reveals that FLC-ORCA outperforms other state-of-the-art methods such as Improved A* and Directional Optimal Reciprocal Collision Avoidance (DORCA). It reduces the overall time taken to complete navigation by 16% and achieves the shortest completion time of 1 min and 38 s, with minimal rerouting (1 occurrence) and the smallest angle change (12°). Our proposed FLC-ORCA challenges assumptions of equal responsibility and enables collision avoidance without pairwise manoeuvres. This approach significantly enhances obstacle avoidance, ensuring safer and more efficient robotic navigation for visually impaired individuals. Article in Journal/Newspaper Orca University of Kent: KAR - Kent Academic Repository Neural Computing and Applications 35 30 22405 22429 |
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University of Kent: KAR - Kent Academic Repository |
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language |
English |
topic |
T Technology |
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T Technology Romlay, Muhammad Ibrahim, Azhar Mohd Toha, Siti Fauziah De Wilde, Philippe Venkat, Ibrahim Ahmad, Muhammad Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
topic_facet |
T Technology |
description |
Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Our FLC-ORCA method assigns responsibility for collision avoidance and predicts the velocity of obstacles using a LiDAR-based mobile robot. We conduct experiments in the presence of static, dynamic, and intelligent entities, recording navigation paths, time taken, angle changes, and rerouting occurrences. The results demonstrate that the proposed FLC-ORCA successfully avoids collisions among objects with different collision avoidance protocols and varying liabilities in circumventing obstacles. Comparative analysis reveals that FLC-ORCA outperforms other state-of-the-art methods such as Improved A* and Directional Optimal Reciprocal Collision Avoidance (DORCA). It reduces the overall time taken to complete navigation by 16% and achieves the shortest completion time of 1 min and 38 s, with minimal rerouting (1 occurrence) and the smallest angle change (12°). Our proposed FLC-ORCA challenges assumptions of equal responsibility and enables collision avoidance without pairwise manoeuvres. This approach significantly enhances obstacle avoidance, ensuring safer and more efficient robotic navigation for visually impaired individuals. |
format |
Article in Journal/Newspaper |
author |
Romlay, Muhammad Ibrahim, Azhar Mohd Toha, Siti Fauziah De Wilde, Philippe Venkat, Ibrahim Ahmad, Muhammad |
author_facet |
Romlay, Muhammad Ibrahim, Azhar Mohd Toha, Siti Fauziah De Wilde, Philippe Venkat, Ibrahim Ahmad, Muhammad |
author_sort |
Romlay, Muhammad |
title |
Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
title_short |
Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
title_full |
Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
title_fullStr |
Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
title_full_unstemmed |
Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA) |
title_sort |
obstacle avoidance for a robotic navigation aid using fuzzy logic controller-optimal reciprocal collision avoidance (flc-orca) |
publisher |
Springer |
publishDate |
2023 |
url |
https://kar.kent.ac.uk/106137/ https://kar.kent.ac.uk/106137/13/Research%20Article%20%5BFLC-ORCA%5D.pdf https://kar.kent.ac.uk/106137/11/Research%20Article%20%5BFLC-ORCA%5D.docx https://doi.org/10.1007/s00521-023-08856-8 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://kar.kent.ac.uk/106137/13/Research%20Article%20%5BFLC-ORCA%5D.pdf https://kar.kent.ac.uk/106137/11/Research%20Article%20%5BFLC-ORCA%5D.docx Romlay, Muhammad, Ibrahim, Azhar Mohd, Toha, Siti Fauziah, De Wilde, Philippe, Venkat, Ibrahim, Ahmad, Muhammad (2023) Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Neural Computing and Applications, 35 . pp. 22405-22429. ISSN 0941-0643. (doi:10.1007/s00521-023-08856-8 <https://doi.org/10.1007/s00521-023-08856-8>) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:106137 </106137>) |
op_doi |
https://doi.org/10.1007/s00521-023-08856-8 |
container_title |
Neural Computing and Applications |
container_volume |
35 |
container_issue |
30 |
container_start_page |
22405 |
op_container_end_page |
22429 |
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1802648757796339712 |