An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing
Seval Capraz, Halil Azyikmis, Adnan Özsoy · International Journal of Machine Learning and Computing · 2020
Usage of multiple unmanned aerial vehicles (UAV) in a certain mission makes flight route planning more complicated and slower.In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques.In addition to this, parallel programming approaches increase the computation performance.Therefore, this study focuses to discuss and solve the route planning problem for multi-UAV systems by using optimization techniques based on an evolutionary algorithm: simulated annealing.The travel cost and execution time are downsized in this work by optimization on algorithm and code.We implemented CPU based parallel solution to compare results with the GPU-accelerated one.The efficiency and the effectiveness of our parallelized and optimized solution is demonstrated through simulations under different scenarios.The results show that our optimized GPU based parallel solution for route planning problem is up to 1.6 times faster than serial and parallel CPU solutions.Moreover, our optimized GPU solution is better on cost than other solutions.It is shown that our GPU based approach is the fastest one and increases performance thanks to the massive parallelization capabilities of GPUs.