To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathem...To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.展开更多
针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该...针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该等级内采样点数量动态调整采样区域,减小搜索范围;利用新节点改进策略使随机树根据环境信息自适应地向目标点调整,并改变扩展步长生成新节点;利用障碍物躲避策略提高算法的目标导向性和躲避障碍物的性能;利用改进的逆向寻优和插入节点并减小转向角的三次B样条曲线对路径进行优化处理。该算法在不同的路径环境中相较于RRT算法的搜索时间和迭代次数均减少了70%以上,且经过优化的路径更短、更平滑。展开更多
针对快速扩展随机树(rapid-exploration random tree^(*),RRT^(*))算法在三维避障路径规划中存在盲目性、低效率和路径不光滑的问题,提出一种改进的RRT^(*)算法,以提高焊接机器人路径规划的性能。通过采用双向搜索策略,缩短搜索时间;结...针对快速扩展随机树(rapid-exploration random tree^(*),RRT^(*))算法在三维避障路径规划中存在盲目性、低效率和路径不光滑的问题,提出一种改进的RRT^(*)算法,以提高焊接机器人路径规划的性能。通过采用双向搜索策略,缩短搜索时间;结合人工势场(artificial potential field,APF)算法与RRT^(*)算法以提升路径平滑性并平衡局部优化与全局最优;提出一种基于角度与密度的改进APF算法策略,提高避障与路径引导效率;提出动态目标偏置策略和动态步长策略,以增强算法在障碍物密集和稀疏区域的自适应性及搜索效率;采用路径修剪策略缩短和平滑路径。最后,通过改进的RRT^(*)算法与RRT^(*)、APF-RRT^(*)、Bi-APF-RRT^(*)(bidirectional-APFRRT^(*))3种算法对比仿真实验以及真机实验,验证了改进算法的高效性和实用性。展开更多
基金Project(60475035) supported by the National Natural Science Foundation of China
文摘To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.
文摘针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该等级内采样点数量动态调整采样区域,减小搜索范围;利用新节点改进策略使随机树根据环境信息自适应地向目标点调整,并改变扩展步长生成新节点;利用障碍物躲避策略提高算法的目标导向性和躲避障碍物的性能;利用改进的逆向寻优和插入节点并减小转向角的三次B样条曲线对路径进行优化处理。该算法在不同的路径环境中相较于RRT算法的搜索时间和迭代次数均减少了70%以上,且经过优化的路径更短、更平滑。
文摘针对快速扩展随机树(rapid-exploration random tree^(*),RRT^(*))算法在三维避障路径规划中存在盲目性、低效率和路径不光滑的问题,提出一种改进的RRT^(*)算法,以提高焊接机器人路径规划的性能。通过采用双向搜索策略,缩短搜索时间;结合人工势场(artificial potential field,APF)算法与RRT^(*)算法以提升路径平滑性并平衡局部优化与全局最优;提出一种基于角度与密度的改进APF算法策略,提高避障与路径引导效率;提出动态目标偏置策略和动态步长策略,以增强算法在障碍物密集和稀疏区域的自适应性及搜索效率;采用路径修剪策略缩短和平滑路径。最后,通过改进的RRT^(*)算法与RRT^(*)、APF-RRT^(*)、Bi-APF-RRT^(*)(bidirectional-APFRRT^(*))3种算法对比仿真实验以及真机实验,验证了改进算法的高效性和实用性。
