Precision of path depends on how exact to get environment information for path planning of mobile robot.
在路径规划中，规划路径的精确程度取决于获取环境信息的准确程度。
Research on local path planning of mobile robot based on Q reinforcement learning and CMAC neural networks.
基于Q强化学习与CMAC神经网络的移动机器人局部路径规划研究。
Hence it has important sense to research intelligence control of tracked mobile robot, path planning and path tracking is key technology in robot research.
因此，研究履带式移动机器人的智能控制具有重要的意义，而路径规划和跟踪控制是机器人研究中的关键技术。
The technologies of intelligent mobile robots involve robot navigation and localization, path planning, motion control, etc.
智能移动机器人技术涉及到机器人导航与定位，路径规划，运动控制等。
The hardware and software of this system can drive effectively the mobile robot to track its preconcerted path and finish motion planning.
该系统的硬件和软件能够有效快速驱动移动机器人跟踪预定的路径以及完成运动规划。
So path planning is one of the important precondition and condition whether mobile robot can finish the task successfully.
因此路径规划是移动机器人能否成功完成任务的重要前提和条件之一。
Path planning technology is one of the important domains in virtual assembling technologies research and mobile robot technologies research.
路径规划技术是虚拟装配技术、移动机器人技术研究中的一个重要领域。
So main researches in the field of mobile robot are focus on multisensor integration and fusion, world model, control architecture, path planning and knowledge learning etc.
因此，移动机器人系统涉及的关键技术包括多传感器数据集成与融合、环境建模、控制体系结构和路径规划以及学习机制等。
This paper presents a global path-planning algorithm of mobile robot under uncertain environment.
本文提出了在不确定的环境中，移动机器人的一种全局路径规划算法。
Based on "giant" mobile robot, the main researching work of this dissertation includes robot kinematics, control system, sense net and its information - fusion and path planning.
本论文基于自行研制开发的移动机器人“巨人”号平台，主要对机器人运动原理、控制系统、感觉网路及其信息融合和路径规划策略进行了研究。
Likewise, these intelligent control theories can also be applied to the path planning of the mobile robot.
同样，这些智能控制理论也能应用于移动机器人的路径规划。
The biologically inspired neural networks based path planning approaches of mobile robot were introduced.
介绍了基于生物激励神经网络的移动机器人路径规划。
Shortest path planning of the planar mobile robot is widely used in many fields.
平面移动机器人最短路径规划算法在许多领域有着十分广泛的应用。
For genetic algorithms based path planning of a mobile robot, a novel fixed-length decimal encoding mechanism for the paths of the mobile robot is proposed in this paper.
针对基于遗传算法的移动机器人路径规划，本文提出了一种新的定长十进制路径编码机制。
Navigation of mobile robot involves path planning, sensor selection and multisensor fusion, etc.
移动机器人导航涉及到路径规划，传感器的选择及传感器信息的融合等技术。
The second method researches dynamic path planning under dynamic environment of mobile robot subjecting to rolling constraint that is involved in the robotic kinematics model.
第二种方法从轮移式机器人的运动学模型出发，研究了受到滚动约束的移动机器人在动态环境中的运动规划问题。
To achieve the navigation in unknown or uncertain circumstance, mobile robot should have the ability of apperceiving the task circumstance and path planning.
移动机器人要实现在未知和不确定环境下自主的工作，应具有感受作业环境和规划自身动作的能力。
Thirdly, basing on characteristics of mobile robot path planning, we designs a kind of Election-survey Algorithm to solve global optimal result of mobile robot path planning.
再次，根据移动机器人路径规划问题的特性，设计出一种新的基于竞选算法的移动机器人全局最优路径规划方法。
The main content of mobile navigation system contains mobile robot localization and path-planning.
移动机器人导航系统主要内容包括移动机器人的定位和路径规划。
The path planning problem of mobile robot is an important composed part of robots' study.
本文针对移动机器人的路径规划问题提出了一种实用的路径规划方法。
In this paper Q reinforcement learning algorithm is adopted for mobile robot local path planning. It makes mobile robot resolve the problem of local path planning in a complex environment.
将Q强化学习算法应用于移动机器人局部路径规划，解决了移动机器人在复杂环境中的局部路径规划问题。
Path planning is the attribute of intelligence for mobile robot.
路径规划是移动机器人智能化的标志。
In this paper, the path planning of single mobile robot and multi-robots under the static and the dynamic environment is mainly studied.
本论文主要研究了单移动机器人和多移动机器人在静态和动态环境下的智能路径规划问题。
A method of path planning based on rolling Windows with function of normal distribution density for mobile robot was presented in this paper.
在原有滚动窗口路径规划方法基础上，提出基于正态密度函数的滚动窗口路径规划方法。
The path planning for intelligent mobile robot has already become the focus of studying in recent years.
近几年来移动机器人的路径规划技术已成为研究热点。
The experiment results showed that improved algorithms 'operating speed and robustness is improved, adaptability of ant colony algorithms on mobile robot path planning is enhanced.
实验结果证明：对基本蚁群算法的改进，提高了运算速度和鲁棒性，增强了蚁群算法在移动机器人路径规划中的适应能力。
The experiment results showed that improved algorithms 'operating speed and robustness is improved, adaptability of ant colony algorithms on mobile robot path planning is enhanced.
实验结果证明：对基本蚁群算法的改进，提高了运算速度和鲁棒性，增强了蚁群算法在移动机器人路径规划中的适应能力。
应用推荐