在移动IP和RSVP相互集成的方案中,为减少RSVP路径切换延迟,降低端端路径代价,公共路由器的选择是一个关键.提出了一个适应的公共路由器选择算法(adaptive common router selectionalgorithm,ACRS),在满足应用连接QoS(quality of service...在移动IP和RSVP相互集成的方案中,为减少RSVP路径切换延迟,降低端端路径代价,公共路由器的选择是一个关键.提出了一个适应的公共路由器选择算法(adaptive common router selectionalgorithm,ACRS),在满足应用连接QoS(quality of service)需求的前提下,利用控制参数λ在RSVP路径切换延迟和端端路径代价之间取得平衡.通过与其他相关RSVP路径切换方案的性能比较,结果表明ACRS可以支持适应的、快速的RSVP路径切换.展开更多
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
文摘在移动IP和RSVP相互集成的方案中,为减少RSVP路径切换延迟,降低端端路径代价,公共路由器的选择是一个关键.提出了一个适应的公共路由器选择算法(adaptive common router selectionalgorithm,ACRS),在满足应用连接QoS(quality of service)需求的前提下,利用控制参数λ在RSVP路径切换延迟和端端路径代价之间取得平衡.通过与其他相关RSVP路径切换方案的性能比较,结果表明ACRS可以支持适应的、快速的RSVP路径切换.
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.
