Optimization-Based Path Planning for Industrial Manipulators: Experimental Validation in Additive Manufacturing
Optimization-Based Path Planning for Industrial Manipulators: Experimental Validation in Additive Manufacturing
This paper introduces an offline optimization-based path planner designed for robotic additive manufacturing, combining joint-path regularity and self-collision penalties with process costs and constraints. The approach handles references of different lengths using full-path and boundary-coupled chunked optimization and employs Iterative Weight Tuning (IWT) to determine cost weights from gaps between Key Performance Indicators (KPIs) and targets. Experimental validation on a UR10e robot demonstrates that planned paths for carbon-fiber winding and robotic 3D printing meet geometric tolerances and KPI targets, with joint-level tracking recorded during execution.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ndary-coupled chunked optimization handle references of different lengths. Iterative Weight Tuning (IWT) determines the cost weights from the gaps between Key Performance Indicators (KPIs) and their targets. In carbon-fiber winding and robotic 3D printing, the planned paths meet the geometric tolerances and all KPI targets, and both paths are executed on a UR10e, without extrusion for printing, with joint-level track
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ing recorded during execution.
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Source：arXiv Robotics — research abstracts · arxiv.org