Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Bi-level programming based real-time path planning for unmanned aerial vehicles
Knowledge-Based Systems
On the performance comparison of multi-objective evolutionary UAV path planners
Information Sciences: an International Journal
Adaptive Dynamic Path Planning Algorithm for Interception of a Moving Target
International Journal of Mobile Computing and Multimedia Communications
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We address the problem of generating feasible paths from a given start location to a goal configuration for multiple unmanned aerial vehicles (UAVs) operating in an obstacle rich environment that consist of static, pop-up and moving obstacles. The UAVs have limited sensor and communication ranges, when they detect a pop-up or a moving obstacle that is in the collision course with the UAV flight path, then it has to replan a new optimal path from its current location to the goal. Determining optimal paths with short time intervals is not feasible, hence we develop anytime algorithm using particle swarm optimization that yields paths whose quality increases with increase in available computation time. To track the given path by the anytime algorithm in 3D, we developed a new uav guidance law that is based on a combination of pursuit guidance law and line of sight guidance law from missile guidance literature. Simulations are carried out to show that the anytime algorithm produces good paths in a relatively short time interval and the guidance law allows the UAVs to track the generated path.