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Application of Path Planning and Obstacle Avoidance for Riverbank Inspection
Jhong-Wei Jhang1, Jih-Gau Juang1
1Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan.
An unmanned aerial vehicle (UAV) system effectively inspects riverbanks for garbage, preventing marine pollution. This technology uses advanced path planning and obstacle avoidance for efficient cleanup operations.
Area of Science:
- Environmental Science
- Robotics and Automation
Background:
- Coastal pollution originates primarily from land-based sources.
- Effective management of riverine waste is crucial for maintaining marine ecological health.
- Current inspection methods may lack the efficiency and adaptability needed for complex riverine environments.
Purpose of the Study:
- To develop and evaluate an unmanned aerial vehicle (UAV) system for riverbank inspection and garbage identification.
- To enhance the efficiency and adaptability of UAVs in complex riverine environments through advanced algorithms.
- To facilitate proactive waste management by identifying pollutants before they reach the sea.
Main Methods:
- Utilized an unmanned aerial vehicle (UAV) for autonomous riverbank inspection.
- Implemented Improved Particle Swarm Optimization (IPSO) with a fuzzy system for optimized path planning.
- Integrated an Artificial Potential Field (APF) algorithm for real-time dynamic obstacle avoidance.
Main Results:
- The UAV system successfully performed riverbank inspections in a complex environment.
- Optimized path planning reduced energy consumption and inspection time.
- The obstacle avoidance system effectively navigated environmental challenges like protruding branches.
Conclusions:
- The proposed UAV system offers a viable solution for monitoring and identifying riverbank waste.
- This technology can significantly contribute to preventing marine pollution by enabling timely cleanup.
- The integration of advanced path planning and obstacle avoidance enhances operational efficiency for environmental monitoring.
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