| Obstacle Avoidance for Multi-UAV Path Planning Based on Particle Swarm optimization |
| Paper ID : 1022-ASAT19-FULL (R3) |
| Authors: |
|
Eslam nabil mobarez *1, mahmoud ashry2, amr sarhan3 138 Emtedad Ramsis 2
Nasr Street 2military technical college 3aircraft electric equipment, military technical college, Cairo, Egypt |
| Abstract: |
| PID genetically tuned and ANFIS control were proposed for Aerosonde UAV in terms of performance and robustness, The comparison is made between these control systems that have been chosen to select the optimal system to be used in the design of the cooperative flight control system. This paper introduces the flight path planning for Multi-UAVs to avoid obstacles depending on how the particle swarm is improved. Optimization problems are improved by using swarm dynamics. This is by describing avoiding obstacles and adapt the path planning for UAVs. The concept of concurrent restructuring has been integrated into path planning to stay away from both static and dynamic obstacles. This optimization technique designed to decrease processing time and the shortest route of the path planning. It's stochastic optimization based on the social behavior of some species to determine the required position in a swarm activity. It gathers local and global search techniques by balancing exploitation and exploration. These particles update themselves with the optimum solutions of their experience as well as the social information collected from other particles. The optimum solutions of the particles are assess by the fitness function of their current position. The velocity of each particle initiate randomly. The location of the Particles changed by updates their velocities. The velocity update is effected by three factors are particles current motion, particles previous experience and the influence of the whole swarm. |
| Keywords: |
| genetically tuned, ANFIS, Cooperative flight control, path planning, particle swarm optimization |
| Status : Conditional Accept (Oral Presentation) |
