Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning
Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group inter...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/3/180 |
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| author | Yidao Ji Qiqi Liu Cheng Zhou Zhiji Han Wei Wu |
| author_facet | Yidao Ji Qiqi Liu Cheng Zhou Zhiji Han Wei Wu |
| author_sort | Yidao Ji |
| collection | DOAJ |
| description | Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments. |
| format | Article |
| id | doaj-art-61faec0daa034e809d2015b474cce4d7 |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-61faec0daa034e809d2015b474cce4d72025-08-20T02:42:45ZengMDPI AGBiomimetics2313-76732025-03-0110318010.3390/biomimetics10030180Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path PlanningYidao Ji0Qiqi Liu1Cheng Zhou2Zhiji Han3Wei Wu4School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInstitute of Unmanned Systems, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaCollege of Engineering, Ocean University of China, Qingdao 266404, ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaUrban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments.https://www.mdpi.com/2313-7673/10/3/180urban dronepath planningswarm intelligence algorithmhuman-inspired algorithm |
| spellingShingle | Yidao Ji Qiqi Liu Cheng Zhou Zhiji Han Wei Wu Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning Biomimetics urban drone path planning swarm intelligence algorithm human-inspired algorithm |
| title | Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning |
| title_full | Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning |
| title_fullStr | Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning |
| title_full_unstemmed | Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning |
| title_short | Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning |
| title_sort | hybrid swarm intelligence and human inspired optimization for urban drone path planning |
| topic | urban drone path planning swarm intelligence algorithm human-inspired algorithm |
| url | https://www.mdpi.com/2313-7673/10/3/180 |
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