Abstract
Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.
Highlights
A unmanned aerial vehicle (UAV) is an aircraft without a pilot on board that can be remotely controlled or flown automatically based on a pre-planned route or automation system [1]
The rapidly-exploring random-tree (RRT) method has been applied to the path planning problem of indoor robots and mini UAVs [6,7,8]
Genetic algorithms (GA) are used to solve the travelling salesman problems related to UAVs, such as maximum information collection [9]
Summary
A UAV is an aircraft without a pilot on board that can be remotely controlled or flown automatically based on a pre-planned route or automation system [1]. For the path planning problem for a high-endurance UAV, the virtual globe platform is a good choice to realize the modelling and visualization of a very large environment. On the virtual globe platform, threat modelling and route planning are more real and effective and mission operations can be performed [21]. On the virtual globe platform, the method of model construction, planning space partitions and the realization of the algorithm will be different from the previous studies in the following ways:. The scale of the terrain data is large on the virtual globe platform In view of this problem, this paper proposes a multi-granularity planning space to achieve a balance between accuracy and efficiency. AA ffuullll ppaatthh ddyynnaammiicc tthhrreeaatt ccaann bbee ddeessccrriibbeedd bbyy {{tthhrreeaatttt11,, tthhrreeaatttt22,, ⋯⋯,,tthhrreeaattttnn}},, wwhheerree
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