Path planning of unmanned aerial vehicle (UAV) is an important preliminary step in UAV flight mission which can be fulfilled by finding the optimum solution for an optimization problem. As a complicated NP-hard search problem, it is necessary to successfully avoid the obstacles while optimizing the flight route according to linear and non-linear constraints. The global search capability of nature-inspired algorithms has made them an attractive choice to address the complexity of UAVs path planning problem. In this work, the UAV path planning problem is modeled as a single objective optimization problem in a static two-dimensional space, where the path is constrained to avoid obstacles. This paper compares the performance of several well-known nature-inspired algorithms on the UAV path planning problem from the earliest to the newest one and shows the interest of DE and TLBO for this path planning problem.
A comparative study of meta-heuristic algorithms for solving UAV path planning
2018-11-01
475782 byte
Conference paper
Electronic Resource
English
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