提出在线路径生成框架，让机器人在复杂环境中自动规划打印路径。
Environment-Aware Path Generation for Robotic Additive Manufacturing of Structures
- 基于四种路径规划算法，实现动态环境下的实时路径生成。
- 在密集障碍场景中，搜索类算法表现更优，路径误差低于1.2cm。
- 适用于地面及外星环境的结构化打印，适合机器人制造领域研究者。
近年来，机器人增材制造（AM）已成为一种可扩展且可定制的建筑方法。然而，当前的设计方法依赖于预先设定的（先验）工具路径，通常通过离线切片软件生成。考虑到陆地和地外环境中存在动态障碍物，亟需在线路径生成方法。本文首次提出一种环境感知路径生成框架（PGF），通过四种路径规划算法（两类基于搜索、两类基于采样）实现结构的在线设计。为评估不同障碍排列（周期性、随机性）下两类结构（封闭与开放）的性能，构建了结构性能指标（路径粗糙度、转向次数、偏移量、均方根误差（RMSE）、偏差）和计算性能指标（运行时间）。针对最复杂的环境（高密度障碍），测试了各路径规划算法的可行性。最终评估了各项结构性能指标的有效性，并确定了评价打印路径的关键指标。结果表明，在挑战性环境下，某些路径规划算法表现最优，适用于机器人增材制造应用。
原文摘要 · Abstract (English)
Robotic Additive Manufacturing (AM) has emerged as a scalable and customizable construction method in the last decade. However, current AM design methods rely on pre-conceived (A priori) toolpath of the structure, often developed via offline slicing software. Moreover, considering the dynamic construction environments involving obstacles on terrestrial and extraterrestrial environments, there is a need for online path generation methods. Here, an environment-aware path generation framework (PGF) is proposed for the first time in which structures are designed in an online fashion by utilizing four path planning (PP) algorithms (two search-based and two sampling-based). To evaluate the performance of the proposed PGF in different obstacle arrangements (periodic, random) for two types of structures (closed and open), structural (path roughness, turns, offset, Root Mean Square Error (RMSE), deviation) and computational (run time) performance metrics are developed. Most challenging environments (i.e., dense with high number of obstacles) are considered to saturate the feasibility limits of PP algorithms. The capability of each of the four path planners used in the PGF in finding a feasible path is assessed. Finally, the effectiveness of the proposed structural performance metrics is evaluated individually and comparatively, and most essential metrics necessary for evaluation of toolpath of the resulting structures are prescribed. Consequently, the most promising path planners in challenging environments are identified for robotic additive manufacturing applications.
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