Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms
In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Biomimetic Intelligence and Robotics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667379724000470 |
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| _version_ | 1846113646901460992 |
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| author | Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan |
| author_facet | Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan |
| author_sort | Timothy Sellers |
| collection | DOAJ |
| description | In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications. |
| format | Article |
| id | doaj-art-61e2eba3be1d42a584003bf00f0f0e5d |
| institution | Kabale University |
| issn | 2667-3797 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomimetic Intelligence and Robotics |
| spelling | doaj-art-61e2eba3be1d42a584003bf00f0f0e5d2024-12-21T04:30:04ZengElsevierBiomimetic Intelligence and Robotics2667-37972024-12-0144100189Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithmsTimothy Sellers0Tingjun Lei1Chaomin Luo2Zhuming Bi3Gene Eu Jan4Department of Electrical and Computer Engineering, Mississippi State University, Starkville 39762, USADepartment of Electrical and Computer Engineering, Mississippi State University, Starkville 39762, USADepartment of Electrical and Computer Engineering, Mississippi State University, Starkville 39762, USA; Corresponding author.Department of Civil and Mechanical Engineering, Purdue University Fort Wayne, Fort Wayne 46805, USADepartment of Computer Science, Asia University, Taichung 413305, Taiwan, ChinaIn the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.http://www.sciencedirect.com/science/article/pii/S2667379724000470Human autonomy teaming (HAT)Robot path planningGeneralized Voronoi diagram (GVD)Spatio-temporal graphsBio-inspired algorithms |
| spellingShingle | Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms Biomimetic Intelligence and Robotics Human autonomy teaming (HAT) Robot path planning Generalized Voronoi diagram (GVD) Spatio-temporal graphs Bio-inspired algorithms |
| title | Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms |
| title_full | Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms |
| title_fullStr | Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms |
| title_full_unstemmed | Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms |
| title_short | Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms |
| title_sort | human autonomy teaming based safety aware navigation through bio inspired and graph based algorithms |
| topic | Human autonomy teaming (HAT) Robot path planning Generalized Voronoi diagram (GVD) Spatio-temporal graphs Bio-inspired algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2667379724000470 |
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