Proceeding for an elucidating draft for the robot’s path planning, there are several aspects which need to be addressed and discuss in detail. Some key points include the definition of the working environment, type of technique used to solve a particular type of problem, their subclassification and interdependencies with each other. It is also required to discuss in detail to understand and solve the problem of path planning in the right perspective. To do so, this paper presents the evolution of path planning algorithms of the last 5 decades, and then a broad classification of all those algorithms. After that, algorithms are categorized into different sections and has been discussed which are very often used in recent times to make the path planning approach more effective and efficient. This paper presents an extensive and elaborative survey of path planning algorithms and associated techniques for robots whose excellent contribution to the filed is invaluable. There are multiple issues related to the working environment of robots, whether it belongs to the two dimensional or three dimensional, static or dynamic, single robot or multi-robot, single objective/multi-objective and many more. This paper addresses all these issues in a detailed way. Another critical issue is related to the scope of algorithms which has been discussed at length in this paper like whether the algorithm is compatible for global/local path planning, it is Exact or heuristic in nature. This issue is systematically and hierarchically described to get a clear understanding of the problem domain. The effort is to bring an insight into the classification and evolution of path planning algorithms with its technical detail and discussions. The paper presents some emerging technologies which can be clubbed with robots to take it to another level. Cloud computing is one which is being extensively absorbed in many technologies. This paper discusses some cloud platforms like OpenStack and Google Cloud to deploy path planning applications for robots. This paper tries explicitly to conclude by testing path planning robots using Google cloud platform (using it as Infrastructure as a Service IaaS) citing its advantages and capability to expand its acceptability and assumes to be the future scope for robot path planning.

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机器人的路径规划：阐明草案

在开始进行机器人路径规划的阐释草案之前，有几个方面需要解决和详细讨论。一些关键点包括工作环境的定义，用于解决特定类型问题的技术类型，其子类别和彼此之间的相互依赖性。还需要详细讨论以正确的角度理解和解决路径规划问题。为此，本文介绍了过去5年中路径规划算法的发展，然后对所有这些算法进行了广泛分类。此后，算法被分为不同的部分，并进行了讨论，这些算法在最近非常常用，以使路径规划方法更加有效。本文对机器人的路径规划算法和相关技术进行了广泛而详尽的调查，这些机器人对机器人领域的杰出贡献是不可估量的。与机器人的工作环境有关的问题很多，无论它是属于二维还是三维的，静态的还是动态的，单机器人还是多机器人，单目标/多目标等等。本文将详细讨论所有这些问题。另一个关键问题与算法的范围有关，本文已对此进行了详尽的讨论，例如该算法是否适用于全局/局部路径规划，本质上是精确还是启发式。系统地和分层地描述了此问题，以使您对问题域有清晰的了解。努力通过其技术细节和讨论来深入了解路径规划算法的分类和演进。本文介绍了一些新兴技术，这些技术可以与机器人结合起来，将其带入另一个层次。云计算是许多技术中广泛采用的一种。本文讨论了一些云平台，例如OpenStack和Google Cloud，它们将为机器人部署路径规划应用程序。本文尝试通过使用Google云平台（将其用作基础架构即服务IaaS）测试路径规划机器人来明确地得出结论，并指出其优势和扩展其可接受性的能力，并认为这是机器人路径规划的未来范围。本文介绍了一些新兴技术，这些技术可以与机器人结合起来，将其带入另一个层次。云计算是许多技术中广泛采用的一种。本文讨论了一些云平台，例如OpenStack和Google Cloud，它们将为机器人部署路径规划应用程序。本文尝试通过使用Google云平台（将其用作基础架构即服务IaaS）测试路径规划机器人来明确地得出结论，并指出其优势和扩展其可接受性的能力，并认为这是机器人路径规划的未来范围。本文介绍了一些新兴技术，这些技术可以与机器人结合起来，将其带入另一个层次。云计算是许多技术中广泛采用的一种。本文讨论了一些云平台，例如OpenStack和Google Cloud，它们将为机器人部署路径规划应用程序。本文尝试通过使用Google云平台（将其用作基础架构即服务IaaS）测试路径规划机器人来明确地得出结论，并指出其优势和扩展其可接受性的能力，并认为这是机器人路径规划的未来范围。