势场法：Real Robots
势场法是一种非常流行的机器人导航模拟方法。然而，有人在真实的机器人上实现了势场法吗？在真实机器人中使用该方法的任何参考或任何声明？
Potential field method is a very popular simulation for Robot Navigation. However, has anyone implemented Potential field method on real robots ? Any reference or any claim of using the method in real robots ?.
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我之前曾做过基于潜在领域的路径规划，但放弃了它，转而采用更合适的方法来解决我的问题。它适用于具有准确定位和准确传感器读数的环境，但在现实环境中则更不适用（即使在速度和路径质量方面，甚至在模拟中，它也不是一个特别好的解决方案）。考虑到现在有很多免费或低成本的优秀 SLAM 实现，我不会费心重新实现，除非您在重用方面遇到非常具体的问题。对于 MRDS（我从事的工作），有 Karto Robotics，ROS 有 SLAM 实现，并且只需 google 搜索即可找到几个开源实现。
如果您想很好地了解路径规划的不同方法，那么您可能需要获取 Segwart 等人撰写的“自主移动机器人简介”的副本。这是一本非常好的书，路径规划部分很好地概述了周围的不同策略。
I have done potential field based path planning before, but abandoned it in favour of more appropriate approaches to my problem. It works adequately for environments where you have accurate localization, and accurate sensor readings, but much less so in real world environments (its not a particulary great solution even in terms of speed and path quality, even in simulation). Considering that there are now a lot of good SLAM implementations available either free or low cost, I wouldnt bother reimplementing unless you have very specific problems with reuse. For MRDS (what i work in) there is Karto Robotics, ROS has a SLAM implementation, and there are several open-source implementations only a google search away.
If you want a good overview of different approaches to path planning, then you might want to grab a copy of "introduction to Autonomous Mobile Robots" by Segwart et al. Its a pretty good book, and the path planning section gives a nice overview of the different strategies around.
如果您对路径或运动普遍感兴趣，我建议您阅读 Steven M. LaValle 所著的规划算法一书规划。
本书中描述的方法在机器人社区中得到了积极的应用。
另一方面，在谷歌学者或 IEEE 网站上搜索，你会得到很多描述势场方法的使用和研究的论文参考文献。
I would suggest reading the book Planning Algorithms by Steven M. LaValle if you are generally interested in path or motion planning.
Methods described in this book are actively used in the robotics community.
A search on google scholar or the IEEE website on the other hand will get you a lot of references to papers describing usage and research of the potential field method.
快速谷歌势场方法提出了这篇论文：移动机器人导航的势场方法及其固有局限性并提醒我上次使用势场方法时遇到的问题。
在我们的项目（CWRU Mobile Robotics）中，我们已经看到了这些潜在的确切问题基于领域的算法。最后一次尝试是在 2009 年参加 IGVC 比赛的移动机器人，也遇到了同样的问题在那篇论文中，特别是局部最小值并且无法穿过间隔很近的障碍物。我清楚地记得，作为 IGVC GPS 航路点导航挑战的一部分，我必须解决间距紧密的障碍物的问题，同时尝试计划穿过栅栏上的狭窄开口。
通过使用自定义 OpenGL 着色器来完成所有计算，同时将势场表示为图像/帧缓冲区，我们能够从算法中获得相当不错的规划速度。正如汤姆指出的那样，它在未知或动态环境中不太好，因为在这些情况下势场永远不会稳定并且需要不断更新。
A quick Google for potential field methods brought up this paper: Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation and reminded me about problems from the last time I worked with a potential field method.
In our projects (CWRU Mobile Robotics), we have seen these exact problems with potential field based algorithms. The last attempt, a mobile robot to compete at IGVC in 2009, had the same issues described in that paper, specifically with local minima and not being able to pass through closely spaced obstacles. I distinctly remember having to work around issues with the closely spaced obstacles while attempting to plan through a narrow opening in a fence as part of the GPS waypoint navigation challenge of IGVC.
We were able to get pretty decent planning speed out of the algorithm by using custom OpenGL shaders to do all of the computation while representing the potential field as an image/framebuffer. As Tom points out, it is not so good in unknown or dynamic environments, as in those situations the potential field will never stabilize and will constantly require updating.
正如@Tom 上面指出的，你通常不能依赖完美的传感器读数或电机将你精确地移动到你认为你告诉它们的距离。
几年前我有机会使用的相对新颖的 SLAM 方法是广义 Voronoi 图 (GVG);基本上，与最近的两堵墙保持等距，继续移动，在与三堵或更多墙等距的点上，返回并在某个点尝试每个两堵墙的分支。您将构建一个图表，让您了解整个房间，并保证您可以看到房间里的所有东西。
As @Tom pointed out above, you can't usually rely on perfect sensor readings or the motors moving you exactly as far as you thought you told them to.
The relatively novel approach to SLAM I had a chance to use years ago was the Generalized Voronoi Graph (GVG); basically, stay equidistant from the nearest two walls, keep moving, and at points where you're equidistant to three or more walls, come back and try each two-wall branch at some point. You'll build a graph that gets you all the way around the room, and guarantees you've had line-of-sight on everything in the room.
2003 年，我们为 DARPA Grand Challenge 车辆（Team Overbot）尝试并放弃了势场算法（OpenSteer）。对于非完整机器人来说这不是一个好主意，因为它不考虑转向或动力学约束。它在狭小的空间中根本无法正常工作。它更适合飞行器，因为飞行器有足够的开放空间并且不想靠近障碍物。
We tried and abandoned a potential field algorithm (OpenSteer) for our DARPA Grand Challenge vehicle (Team Overbot) in 2003. It's not a good idea for a non-holonomic robot, since it doesn't consider steering or dynamics constraints. It doesn't work at all well in tight spaces. It's better suited to flying machines, where you have plenty of open space and don't want to get close to obstacles.