Constrained Heterogeneous Vehicle Path Planning for Large-area Coverage

There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVs' battery life and communication distance, complete coverage of large areas typically involves multiple take-offs and landings to recharge ba...

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Vydáno v:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems s. 4113 - 4120
Hlavní autoři: Deng, Di, Jing, Wei, Fu, Yuhe, Huang, Ziyin, Liu, Jiahong, Shimada, Kenji
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2019
Témata:
Autonomous aerial vehicles
Batteries
Genetic algorithms
Intelligent robots
Land vehicles
Path planning
Planning
Transportation
Traveling salesman problems
ISSN:2153-0866
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Shrnutí:There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVs' battery life and communication distance, complete coverage of large areas typically involves multiple take-offs and landings to recharge batteries, and the transportation of UAVs between operation areas by a ground vehicle. In this paper, we introduce a novel large-area-coverage planning framework which collectively optimizes the paths for aerial and ground vehicles. Our method first partitions a large area into sub-areas, each of which a given fleet of UAVs can cover without recharging batteries. UAV operation routes, or trails, are then generated for each sub-area. Next, the assignment of trials to different UAVs and the order in which UAVs visit their assigned trails are simultaneously optimized to minimize the total UAV flight distance. Finally, a ground vehicle transportation path which visits all sub-areas is found by solving an asymmetric traveling salesman problem (ATSP). Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. We show that the solution time can be reduced to close-to-real-time levels by first finding a feasible solution using a Random Key Genetic Algorithm (RKGA), which is then locally optimized by solving a much smaller MIQP.
ISSN:2153-0866
DOI:10.1109/IROS40897.2019.8968299