This work aims to present a navigation scheme whose topmost layer further considers the presence of obstacles and steers the aircraft towards a safe path by altering its navigation references. The complete framework is divided into two control layers carrying out the orientation and navigation, while a third obstacle-aware layer, which is based on a Virtual Leader-Follower coordination problem, generates the positioning references for the navigation controller. This outline allows designing the inner control layers independently, without obstacle awareness, giving flexibility to different combinations of UAVs and controller laws. To validate the proposed structure, numerical simulations are performed where moving obstacles are considered. In addition, experimental testing is carried out by implementing the three-layer navigation in a quadrotor aircraft, performing an independent trajectory while in the presence of a static obstacle. Both tests show acceptable results, and the obstacle collision avoidance is reached.
This research work addresses the problem of path planning for unmanned aerial vehicles (UAVs) in complex environments such as a maze. A genetic algorithm (GA) with variable chromosomes and gene change conditions is designed when the decision point criteria and a collision with a wall are presented; without considering large chromosomes. Our proposed GA obtains the sequence of minimum movements required to solve the maze. Then, the trajectories are generated by high-order polynomials. Finally, numerical simulations and real-time tests are carried out to validate the proposed algorithm.
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