Stealth attraction techniques summarized for free

Furthermore, an optimized path guarantees the successful completion of the mission with the minimized cost. īased on the mastery degree of the surrounding information, path planning can be constructed by two planning engines including a global path planning engine that uses prior environmental information for global planning and a local path planning engine that responds to the real-time sensor information, such as size and location of an obstacle.

The local path planning is suitable for the situation where a starting point and a destination are relatively close. The local path planning is quick, real-time, and responsive, but it is easily distracted by local information thus falling into a local optimal solution without achieving the global target. Global path planning provides solution for overall path finding which finds out a both feasible and optimal path based on the environmental information obtained. All the environmental information is acquired firstly and then a preliminary (global) path planning is carried out according to the environmental map constructed. The global path planning relies on global environmental information and cannot deal with real-time problems during planning process. Global path planning demands large computational capacity. Meanwhile, local path planning is a kind of dynamic programming while global path planning belongs to static programming. Ĭonsiderable literatures have been reported on the subject of path planning. The main methods currently developed for path planning include graph-based, population-based evolutionary algorithms, and distributed approaches. The graph-based algorithms include Voronoi diagram search method, directed graph-based method, A search algorithms, mathematical programming method, artificial potential field method (APF), and D lite algorithms. The graph-based algorithms, however, have shown weakness in practical situations due to its difficulty to address the motion constraints of UAV. The population-based algorithms contain particle swarm optimization (PSO), ant colony optimization (ACO), intelligent water drops optimization (IWD), gravitational search algorithm (GSA), and artificial bee colony (ABC).

The population-based evolutionary algorithms have advantages of reduced complexity and dimensions in the planning phase. Distributed path planning off loads the path planning to environment using message exchanges between intelligent nodes.

Stealth attraction techniques summarized for free