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 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.
整个避碰规划系统包括声纳探测目标、目标跟踪、特征信息提取、目标运动估计、避碰算法等部分。
The system includes such modules as obstacle detection by FLS, tracking, motion estimation and planning algorithms.
为克服传统人工势场在动态未知环境下机器人避碰规划中存在的缺陷，提出人工协调场法(acf)。
To overcome the drawbacks of the conventional artificial potential fields in motion planning of mobile robots for dynamic uncertain environments, an artificial coordinating field (ACF) is proposed.
主要采用强化学习的方法对AUV进行控制和决策，综合Q学习算法、BP神经网络和人工势场法对AUV进行避碰规划。
The reinforcement learning is adopted to control and decision for AUV, and Q-learning, BP neural net, artificial potential is integrated to avoidance planning for AUV.
介绍了一种双机器人时间优化的避碰轨迹规划方法。
A kind of track planning method for time-optimal and collision avoidance dual robots was introduced.
在路径规划中应用了改进人工势场法并对其进行了仿真试验，结果表明其算法对于机器人的避碰有了可观的改善。
The improvement artificial potential field law in the way plan was applied and simulated, and finally results indicated this algorithm considerably improved the robot avoiding collision.
在规划过程中，机器人有两种行为：向目标运动和避碰，且避碰行为具有优先权。
The robot has two behaviors in the course of planning: moving to the goal and collision avoidance, and the collision avoidance behavior has the higher priority.
为了解决多机器人的避碰问题，文中提出了一种基于运动规划的多机器人协调避碰方法。
In order to solve the collision avoidance problem of multi-robots, this dissertation propose a coordination collision avoidance method of multi-robots based on the movement planning.
为了解决多机器人的避碰问题，文中提出了一种基于运动规划的多机器人协调避碰方法。
In order to solve the collision avoidance problem of multi-robots, this dissertation propose a coordination collision avoidance method of multi-robots based on the movement planning.
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