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1)  Ameliorative artificial potential field
改进人工势场
2)  improved potential field
改进人工势场法
3)  modified potential field
改进势场法
1.
Anti-collision path planning for mobile robot based on modified potential field method
基于改进势场法的移动机器人避障路径规划
2.
Aiming at the limitation of the traditional artificial potential field method,a modified potential field method is proposed.
分析了机器人路径规划方法中的人工势场法的不足,提出了改进势场法。
4)  artificial potential field
人工势场
1.
A path planning method for mobile robot based on artificial potential field;
基于人工势场法的机器人路径规划
2.
Navigation of mobile robot using improved artificial potential field method;
改进的人工势场法用于移动机器人导航
3.
Research on Reinforcement Learning Problem Based on Artificial Potential Field;
基于人工势场的激励学习问题研究
5)  potential field
人工势场
1.
The artificial potential field method is widely used for mobile robot path planning due to its simplicity and mathematical analysis,However,most reaserches have been focused on solving the path planning in a stationary environment where both targets and obstacles are stationary.
人工势场法由于其简单性和便于数学描述被广泛应用在移动机器人路径规划上,然而多数研究都集中在解决静态路径规划上,即目标和障碍物都是静态的。
2.
Secondly, this thesis introduces some popular methods for path planning, such as the global path planning methods, including artificial potential field, topology, visual graph and free-space method; including the local path planning methods, neural network, fuzzy logic and genetic algorithm and analyzes the advantages and weakpoints of these meth
其次,概括介绍了机器人路径规划的方法,包括全局路径规划方法:人工势场法,栅格法,拓扑法,可视图法,自由空间法,以及局部路径规划方法:神经网络法,模糊逻辑算法,遗传算法等。
6)  artificial potential
人工势场
1.
On the basis of building forward looking sonar view model and artificial potential model,decision control of AUV is made,objects are searched through grads approach,a collision free path is planed.
在建立前视声呐视域模型和人工势场模型的基础上,对AUV进行决策控制,通过梯度逼近法对目标进行搜索,快速规划出一条无碰撞的路径。
2.
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.
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。
3.
The reinforcement learning is adopted to control and decision for Autonomous Underwater Vehicles (AUV), and Q- learning , BP neural net, artificial potential is integrated to local plan for AUV.
主要采用强化学习的方法对自治水下机器人(AUV)进行控制和决策,综合Q学习算法、BP神经网络法、人工势场法对AUV进行局部路径规划。
补充资料:改进
改变旧有情况,使有所进步:~工作ㄧ操作方法有待~。
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