Abstract
Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 km2 virtual city and 12 km2 real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
Highlights
In recent years, unmanned aerial vehicles (UAVs) have been a crucial component of future delivery, service patrol, and emergency response applications
Aiming at multi-UAV system optimization and conflict avoidance for regional multitask execution, we propose a bilevel model that is defined as multi-UAV cooperative trajectory planning (MCTP) to tackle this problem
Multi-UAV trajectory planning is of great importance to future delivery, service patrol, and emergency response services
Summary
In recent years, unmanned aerial vehicles (UAVs) have been a crucial component of future delivery, service patrol, and emergency response applications. The graph-based method [27,28], TDTSPP [19,20], CBS [22,23], and SIPP [24] are all able to avoid conflicts; when the graph size or the number of robots exceeds certain limitations, the complexities grow exponentially, which makes it impossible to solve These studies are based on designated paths of polylines with an average speed, without considering the motion plan of UAV flying states, which is crucial in UAV spatiotemporal trajectory planning.
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