以具有精英保留的免疫遗传算法(Immune genetic algorithm with elitism,IGAE)和栅格法为基础,提出一种新的移动机器人最优路径规划方法。其步骤为:首先采用栅格法对机器人工作空间进行划分,建立给定环境中移动机器人的自由空间模型;每...以具有精英保留的免疫遗传算法(Immune genetic algorithm with elitism,IGAE)和栅格法为基础,提出一种新的移动机器人最优路径规划方法。其步骤为:首先采用栅格法对机器人工作空间进行划分,建立给定环境中移动机器人的自由空间模型;每个栅格用1个序号标识,并以路径上各栅格序号作为机器人路径的编码参数。然后,采用直角坐标和序号混合应用的方法产生初始种群,群体中每1个个体表示1条机器人路径,采用IGAE算法对种群进行优化,最终找出最优路径。为了保持种群初始化和遗传操作过程中个体所对应的路径的连续性和避障要求,在IGAE算法中引入删除、插入算子。计算机仿真实验结果表明,所提出的方法比基于全局收敛型遗传算法的路径规划方法更加快速和有效。展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the conf...Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.展开更多
文摘以具有精英保留的免疫遗传算法(Immune genetic algorithm with elitism,IGAE)和栅格法为基础,提出一种新的移动机器人最优路径规划方法。其步骤为:首先采用栅格法对机器人工作空间进行划分,建立给定环境中移动机器人的自由空间模型;每个栅格用1个序号标识,并以路径上各栅格序号作为机器人路径的编码参数。然后,采用直角坐标和序号混合应用的方法产生初始种群,群体中每1个个体表示1条机器人路径,采用IGAE算法对种群进行优化,最终找出最优路径。为了保持种群初始化和遗传操作过程中个体所对应的路径的连续性和避障要求,在IGAE算法中引入删除、插入算子。计算机仿真实验结果表明,所提出的方法比基于全局收敛型遗传算法的路径规划方法更加快速和有效。
文摘A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
基金This work was partially supported by National Key R&D Program of China(2019YFB1312400)Shenzhen Key Laboratory of Robotics Perception and Intelligence(ZDSYS20200810171800001)+1 种基金Hong Kong RGC GRF(14200618)Hong Kong RGC CRF(C4063-18G).
文摘Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.
