Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are con...Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.展开更多
基金This project is supported by National Science Foundation for Young Scientists of China(61701322)the Key Projects of Liaoning Natural Science Foundation(20170540700)+3 种基金the Key Projects of Liaoning Provincial Department of Education Science Foundation(L201702)Liaoning Natural Science Foundation(201502008,20102175)the Program for Liaoning Excellent Talents in University(LJQ2012011)the Liaoning Provincial Department of Education Science Foundation(L201630).
文摘Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.
