Autonomous robots can use path planning techniques to determine the optimal trajectory during the mission. These techniques can be classified as classical, meta heuristic, or machine learning-based. The choice of each technique for a mission depends on its specific requirements, such as finding the shortest path, completing the mission in the minimum time, or/and exploring the environment, among others. Therefore, the path planning algorithms analysis is essential to assist in selecting the appropriate technique. In the literature, the path planning algorithms are typically compared within the same category, and a general analysis is conducted to decide which technique to employ for a particular mission. However, this paper aims to delve deeper into the behavior and performance of these three path planning techniques. The analysis is based on simulations in various environments to understand how each technique behaves and performs, specifically focusing on computation costs, time, and path length efficiency.

自主机器人可以使用路径规划技术来确定任务期间的最佳轨迹。这些技术可以分为经典技术、元启发式技术或基于机器学习的技术。每种任务技术的选择取决于其具体要求，例如寻找最短路径、在最短时间内完成任务或/和探索环境等。因此，路径规划算法分析对于帮助选择合适的技术至关重要。在文献中，路径规划算法通常在同一类别内进行比较，并进行一般分析以确定针对特定任务采用哪种技术。然而，本文旨在更深入地研究这三种路径规划技术的行为和性能。