Obstacle avoidance path planning for free flying space robot is realized by the use of ant algorithm.
本文采用蚁群算法实现了自由飞行空间机器人的避障路径规划。
A path planning algorithm based on virtual target and an algorithm of obstacle avoidance are presented in decision making subsystem of robot.
针对足球机器人运动的决策子系统，提出了基于虚拟目标点的路径规划方法和避障算法。
The obtained results can not only been applied to the design and implementation of algorithm for avoidance obstacle path planning, but also been used to solve the problems of computational geometry.
取得的结果不仅可直接应用于避障路径规划算法的设计与实现，而且还可应用于计算几何中相关问题的求解。
Most of them were theory researches, mainly on kinematical planning and optimization, especially in the case of obstacle avoidance.
其中大部分是理论研究，主要集中于运动学的规划与优化，尤其是避障的情况下的研究。
The complete coverage path planning with dynamic obstacle avoidance for mobile robots can be efficiently implemented by the proposed method in uncertain dynamic environments.
该方法能在不确定动态环境中有效地实现机器人自主避障的完全遍历路径规划。
The whole system includes two parts, the global path planning and the local planning for obstacle avoidance.
采用栅格法建立机器人空间模型，整个系统由全局路径规划和局部避碰规划两部分组成。
The researches on collaboration strategy of MAS are focus on path planning, cooperative localization and cooperative obstacle avoidance.
多智能体协作策略的研究主要集中在路径规划，协作定位和协同避障等方面。
Aiming at the problem of real-time obstacle avoidance for autonomous virtual humans in dynamic unknown environment, this paper presents a path planning model based on the mixed perception information.
针对动态未知环境下的自主虚拟人实时避障问题，提出一种基于混合感知信息的路径规划模型。
Q learning method is used in intelligence planning path with magnets to achieve the shortest path search, obstacle avoidance, task scheduling and so on.
采用Q学习方法进行磁钉路径的智能规划，实现最短路径寻找，同时解决了任务调度及避障等问题。
A new sensor-based obstacle avoidance and path planning algorithm for mobile robots is proposed.
提出一种基于传感器的移动机器人避障路径规划算法。
The whole system includes two parts: the global path planning and the local planning for obstacle avoidance.
采用栅格法建立了机器人工作空间模型，整个系统由全局路径规划和局部避碰规划两部分组成。
The whole system includes two parts: the global path planning and the local planning for obstacle avoidance.
采用栅格法建立了机器人工作空间模型，整个系统由全局路径规划和局部避碰规划两部分组成。
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