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Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.
Wenyu Cai1, Meiyan Zhang2, Yahong Rosa Zheng3
1School of Electronics & Information, Hangzhou Dianzi University, Hangzhou 310018, China. dreampp2000@163.com.
Sensors (Basel, Switzerland)
|July 12, 2017
Summary
This study addresses multi-AUV task assignment and path planning in 3D underwater networks. It integrates 3D Dubins curves with the Multiple Traveling Sales Person (MTSP) model for efficient, constraint-aware mission execution.
Area of Science:
- Robotics and Autonomous Systems
- Underwater Sensor Networks
- Path Planning Algorithms
Background:
- Multiple Autonomous Underwater Vehicles (AUVs) face complex task assignment and path planning challenges in 3D environments.
- Nonholonomic motion constraints in 3D space significantly complicate AUV trajectory generation.
Purpose of the Study:
- To develop an effective method for task assignment and path planning for multiple AUVs in 3D underwater wireless sensor networks.
- To address the limitations of linear interpolation for generating continuous 3D paths.
Main Methods:
- Modeling the problem as a Multiple Traveling Sales Person (MTSP) problem.
- Utilizing the Genetic Algorithm (GA) to solve the MTSP with Euclidean distance and Tour Length Balance (TLB) constraints.
- Mapping 2D Dubins curves to 3D and applying dynamic constraint checks for path finalization.
Main Results:
- Linear interpolation of 2D Dubins curves into 3D paths was found to lack G1 continuity.
- A method for selecting valid 3D Dubins curves that satisfy AUV dynamics constraints was successfully implemented.
- The integration of 3D Dubins curves with the MTSP model proved effective for 3D target assignment and path planning.
Conclusions:
- The proposed approach effectively solves the 3D task assignment and path planning problem for multiple AUVs.
- Constraint-aware path planning is crucial for realistic AUV operations in 3D underwater environments.
- The study demonstrates a viable method for generating dynamically feasible trajectories for multi-AUV missions.
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