如何避免机器人陷入局部极小值？
我有一段时间致力于机器人的运动规划，并且有一段时间想探索改善“势场”方法提供的机会的可能性。我的挑战是避免机器人在使用“势场”方法时陷入“局部最小值”。我没有使用“随机游走”方法来避免机器人被困，而是考虑是否可以实现 A* 的变体，它可以作为一种指南，精确地避免机器人被困在“局部最小值”。
有没有类似的经验，或者可以参考文献，比“随机游走”的方法更有效地避免局部极小值。
I have some time occupying myself with motion planning for robots, and have for some time wanted to explore the possibility of improving the opportunities as "potential field" method offers. My challenge is to avoid that the robot gets trapped in "local minimum" when using the "potential field" method. Instead of using a "random walk" approach to avoid that the robot gets trapped I have thought about whether it is possible to implement a variation of A* which could act as a sort of guide for precisely to avoid that the robot gets trapped in "local minimum".
Is there some of the experiences of this kind, or can refer to literature, which avoids local minimum in a more effective way than the one used in the "random walk" approach.
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A*和潜在字段都是搜索策略。您遇到的问题是，某些搜索策略比其他策略更“贪婪”，而且过于贪婪的算法往往会陷入局部最小值。
在一些替代方案中，贪婪（陷入局部最小值的主要原因）和多样性（尝试短期内似乎不是一个好的选择的新替代方案）之间的紧张关系被参数化。
几年前，我研究了一些关于蚂蚁算法的知识（搜索 Marco Dorigo、ACS、ACO），他们有一系列搜索算法，可以应用于几乎任何事物，并且它们可以控制贪婪与探索您的搜索空间。在他们的一篇论文中，他们甚至比较了使用遗传算法、模拟退火等解决 TSP（典型旅行商问题）的搜索性能。蚂蚁赢了。
我过去使用遗传算法解决了 TSP，如果您愿意的话，我仍然有 delphi 中的源代码。
A* and potential fields are all search strategies. The problem you are experiencing is that some search strategies are more "greedy" than others, and more often than not, algorithms that are too greedy get trapped in local minimum.
There are some alternatives where the tension between greediness (the main cause of getting trapped on local minimum) and diversity (trying new alternatives that don't seem to be a good choice in the short term) are parameterized.
A few years ago I've researched a bit about ant algorithms (search for Marco Dorigo, ACS, ACO) and they have a family of search algorithms that can be applied to pretty much anything, and they can control the greediness vs. exploration of your search space. In one of their papers, they even compared the search performance solving the TSP (the canonical traveling salesman problem) using genetic algorithms, simulated annealing and others. Ant won.
I've solved the TSP in the past using genetic algorithms and I still have the source code in delphi if you like.
使用调和函数路径规划。调和函数是描述流体流动和其他自然现象的势函数。如果使用边界条件正确设置它们，则它们没有局部最小值。自 90 年代初以来，Rod Grupen 和 Chris Connolly。这些函数已被证明是优化控制的一种特定形式，可以最大限度地减少碰撞概率。它们可以使用差分方程（即高斯-赛德尔、连续过度松弛等）在低维空间中有效地计算。
Use harmonic function path planning. Harmonic functions are potential functions that describe fluid flow and other natural phenomena. If they are setup correctly using boundary conditions, then they have no local minima. These have been in use since the early 90s by Rod Grupen and Chris Connolly. These functions have been shown to be a specific form of optimal control that minimizes collision probabilities. They can be computed efficiently in low dimensional spaces using difference equations (i.e. Gauss-seidel, successive over-relaxation, etc.).