Smooth Curve Fitting of Mobile Robot Trajectories Using Differential Evolution
Smooth Curve Fitting of Mobile Robot Trajectories Using Differential Evolution
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DOI：
10.1109/access.2020.2991003
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Miyashita，Tomoyuki
中科院分区：
文献类型：
--
作者：
Parque，Victor;Miyashita，Tomoyuki
近年来，移动机器人在各个领域的应用引起了人们的广泛关注。在自主导航的背景下，路径规划与舒适性、安全性、执行时间和节能相关。在本文中，我们提出了一种利用不同的初始化、选择压力、探索和开发模式的差异进化来优化拟合和平滑准则，从而从观察到的机器人轨迹中提出平滑路径的方法。我们使用Boe-Bot移动机器人体系结构中的一组真实机器人轨迹进行了严格的计算实验，表明了该方法在计算光滑曲线方面的可行性和效率，表明了基于机器人轨迹的三角形凸壳的贪婪初始化方案以及基于等级和参数自适应的差分进化(RBDE)、带外部档案的自适应差分进化(JADE)和策略自适应差分进化(SADE)等差分进化方案具有优越的性能。我们获得的结果为进一步开发数据驱动的曲线拟合和路径规划算法提供了基础，这些算法可能会在机器人学和运筹学的几个实际应用中使用。
Mobile robots have recently attracted the attention and applicability in field areas ubiquitously. Within the context of autonomous navigation, path planning is relevant for comfortability, safety, execution time and energy savings. In this paper, we propose an approach to suggest smooth paths from observed robot trajectories by optimizing fitting and smoothness criteria using Differential Evolution with distinct modes of initialization, selection pressure, exploration and exploitation. Our rigorous computational experiments using a relevant set of real-world robot trajectories from the Boe-Bot mobile robot architecture show the feasibility and efficiency of our approach in computing smooth curves, suggesting the superior performance of the greedy initialization scheme based on the triangular convex hull of the robot trajectory, and Differential Evolution with exploitative and parameter adaptation schemes such as Rank-Based Differential Evolution (RBDE), Adaptive Differential Evolution with External Archive (JADE) and Strategy Adaptation Differential Evolution (SADE). Our obtained results offer the building blocks to further advance towards developing data-driven curve fitting and path planning algorithms, which may find use in several real-world applications in Robotics and Operations Research.