Environment-Aware Path Generation for Robotic Additive Manufacturing of Structures

TL;DR

This paper introduces an environment-aware path generation framework for robotic additive manufacturing that dynamically plans toolpaths in obstacle-rich environments using multiple algorithms, improving feasibility and structural quality.

Contribution

It presents the first online, environment-aware path planning framework utilizing four different algorithms for robotic AM, addressing dynamic obstacles and environment variability.

Findings

Sampling-based algorithms outperform search-based in dense obstacle environments.

Most promising path planners identified for challenging environments.

Structural and computational metrics effectively evaluate path feasibility and quality.

Abstract

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.