针对复杂输电环境下机械臂多目标点路径规划效率低、路径代价高的问题,提出了基于改进的人工势场引导的知情快速扩展随机树算法(improved artificial potential field-informed rapidly-exploring random trees star,IAPF-IRRT^(*))来...针对复杂输电环境下机械臂多目标点路径规划效率低、路径代价高的问题,提出了基于改进的人工势场引导的知情快速扩展随机树算法(improved artificial potential field-informed rapidly-exploring random trees star,IAPF-IRRT^(*))来提升路径规划的性能。首先引入长方体斥力场模型改进传统人工势场中球形斥力场模型,建立输电环境下复杂障碍物的斥力场。然后采用位置均匀分布的椭球域改进IAPF-IRRT*算法中的椭圆域,避免复杂输电环境下采样点出现局部冗余,提高搜索效率。最后引入三角寻优法优化路径中的冗余节点并结合三次样条插曲线对路径平滑处理。在三维简单、三维复杂和复杂输电环境这三组不同复杂程度的障碍物地图上进行验证,其结果表明:IAPF-IRRT*算法与标准RRT、RRT*算法相比,时间效率分别提升了44.8%~83.8%、68.3%~95.2%、26.5%~71.8%;路径代价分别降低了15.5%~35.0%、14.1%~35.3%、31.5%~43.5%;路径中的节点数量分别减少了75.6%~78.8%、75.0%~78.0%、70.4%~72.0%。展开更多
A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MC...A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.展开更多
文摘针对复杂输电环境下机械臂多目标点路径规划效率低、路径代价高的问题,提出了基于改进的人工势场引导的知情快速扩展随机树算法(improved artificial potential field-informed rapidly-exploring random trees star,IAPF-IRRT^(*))来提升路径规划的性能。首先引入长方体斥力场模型改进传统人工势场中球形斥力场模型,建立输电环境下复杂障碍物的斥力场。然后采用位置均匀分布的椭球域改进IAPF-IRRT*算法中的椭圆域,避免复杂输电环境下采样点出现局部冗余,提高搜索效率。最后引入三角寻优法优化路径中的冗余节点并结合三次样条插曲线对路径平滑处理。在三维简单、三维复杂和复杂输电环境这三组不同复杂程度的障碍物地图上进行验证,其结果表明:IAPF-IRRT*算法与标准RRT、RRT*算法相比,时间效率分别提升了44.8%~83.8%、68.3%~95.2%、26.5%~71.8%;路径代价分别降低了15.5%~35.0%、14.1%~35.3%、31.5%~43.5%;路径中的节点数量分别减少了75.6%~78.8%、75.0%~78.0%、70.4%~72.0%。
基金the Natural Science Foundation of Anhui Province of China (050420212)the Excellent Youth Science and Technology Foundation of Anhui Province of China (04042069).
文摘A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.
