| 1. | Anti - collision path planning for soccer robot using modified potential field method
基于改进势场法的足球机器人避障路径规划 |
| 2. | Mobile robot path planning using modified potential field method in dynamic environment
动态环境中基于改进势场法的移动机器人路径规划 |
| 3. | Study of local path planning of mobile robot based on improved artificial potential field method
基于改进人工势场法的移动机器人局部路径规划的研究 |
| 4. | The second part describes the problem of goals non - reachable with obstacles nearby ( gnron ) when using potential field methods for mobile robot path planning
其次,研究了传统khatib人工势场模型的不可到达问题( gnron ) 。 |
| 5. | Firstly , the research of this paper is focus on preflight global planning algorithm based on cell decomposition methods and artificial potential field methods
本文首先对基于栅格法和人工势场法思想的离线航迹规划算法进行了研究。 |
| 6. | It takes the global properties of path planning into consideration , and overcomes the shortcoming of 1ocai optimum in some local path planner , e . g . artificial potential field method
这种方法在局部规划的同时，兼备了路径规划的全局性，有效地从根本上避免了人工势场法等局部分析方法容易陷入局部最优的不足。 |
| 7. | This paper firstly analyses the limitations of traditional potential field methods in dynamic environment , and based on this analysis , the traditional potential field is improved by introducing the concept of velocity potential field
摘要分析了传统势场法在动态环境下的不足，并在此基础上引入了速度势场的概念，改进了传统的势场函数，推导出新的引力函数和斥力函数。 |
| 8. | The soccer robot system is a dynamic environment with multiple obstacles . it is a problem of high complexity to perform path planning in such environments . the traditional methods are not efficient in such complex environments . in this paper , a self - learning method of robot navigation is proposed based on the reinforcement learning method and artificial potential field method
本论文将增强式学习算法和人工势场法相结合，提出状态评价函数和势场的对应关系，以及控制策略和势场力方向的对应关系，通过机器人的自适应学习，来形成优化的人工势场，使机器人能够以最短路径绕过障碍，到达目标。 |
| 9. | The control policy of the behaviors such as avoiding and pursuing is the combination of potential field method and behavior fusion method . the reinforcement learning based on existing knowledge is used to modify the importance parameters of each behavior , which will avoid the disadvantage of rules
在对行为层规划的研究中，运用基于势场法思想的行为融合方法实现多机器人编队的避碰和追踪策略，并用嵌入先验知识的强化学习方法对融合过程中的重要性参数进行调整，弥补了基于规则设计的不完整性。 |
