基于深度学习编码器模型的路径规划算法性能改进
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model
摘要：
目前，路径规划算法被用于许多日常任务中。它们与在交通中找到最佳路线并使自主机器人能够导航有关。路径规划的使用在大型和动态环境中存在一些问题。大型环境使得这些算法需要花费大量的时间来寻找最短路径，而动态环境则要求每次环境发生变化时都重新执行算法，从而增加了算法的执行时间。降维似乎是这个问题的解决方案，在这种情况下，这意味着删除这些环境中存在的无用路径。大多数降维算法仅限于输入数据的线性相关。最近，卷积神经网络（CNN）编码器被用来克服这种情况，因为它可以使用线性和非线性信息来减少数据。本文深入分析了使用这种CNN编码器模型消除无用路径的性能。为了衡量上述模型的效率，我们将其与不同的路径规划算法相结合。接下来，在由五个场景组成的数据库中检查最终算法（组合和未组合）。每个场景都包含固定和动态障碍。他们提出的模型，CNN编码器，与文献中其他现有的路径规划算法相关联，与分析的所有路径规划算法相比，能够减少找到最短路径的时间。平均下降时间为54.43%。
英文摘要：
Currently, path planning algorithms are used in many daily tasks. They are relevant to find the best route in traffic and make autonomous robots able to navigate. The use of path planning presents some issues in large and dynamic environments. Large environments make these algorithms spend much time finding the shortest path. On the other hand, dynamic environments request a new execution of the algorithm each time a change occurs in the environment, and it increases the execution time. The dimensionality reduction appears as a solution to this problem, which in this context means removing useless paths present in those environments. Most of the algorithms that reduce dimensionality are limited to the linear correlation of the input data. Recently, a Convolutional Neural Network (CNN) Encoder was used to overcome this situation since it can use both linear and non-linear information to data reduction. This paper analyzes in-depth the performance to eliminate the useless paths using this CNN Encoder model. To measure the mentioned model efficiency, we combined it with different path planning algorithms. Next, the final algorithms (combined and not combined) are checked in a database that is composed of five scenarios. Each scenario contains fixed and dynamic obstacles. Their proposed model, the CNN Encoder, associated to other existent path planning algorithms in the literature, was able to obtain a time decrease to find the shortest path in comparison to all path planning algorithms analyzed. the average decreased time was 54.43 %.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
