Journal of the ACM (JACM)
Communications of the ACM
Eighteenth national conference on Artificial intelligence
Theta*: any-angle path planning on grids
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Minimum Risk Approach for Path Planning of UAVs
Journal of Intelligent and Robotic Systems
Evolutionary algorithm based offline/online path planner for UAV navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multi-UAS Cooperative Mission Over Non-Segregated Civil Areas
Journal of Intelligent and Robotic Systems
Three-dimensional Route Planning for Unmanned Aerial Vehicles in a Risk Environment
Journal of Intelligent and Robotic Systems
Adaptive Dynamic Path Planning Algorithm for Interception of a Moving Target
International Journal of Mobile Computing and Multimedia Communications
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The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on post-processing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with "any" heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A Matlab based planning tool was developed, collecting four separate approaches: geometric predefined trajectories, manual waypoint definition, automatic waypoint distribution (i.e. optimizing camera payload capabilities) and a comprehensive A*-based algorithm used to generate paths, minimizing risk of collision with orographic obstacles. The tool named PCube exploits Digital Elevation Maps (DEMs) to assess the risk maps and it can be used to generate waypoint sequences for UAVs autopilots. In order to improve the A*-based algorithm, the solution is extended to tri-dimensional environments implementing a more effective graph search (based on Theta*). In this paper the application of basic Theta* to tri-dimensional path planning will be presented. Particularly, the algorithm is applied to orographic obstacles and in urban environments, to evaluate the solution for different kinds of obstacles. Finally, a comparison with the A* algorithm will be introduced as a metric of the algorithm performances.