AI & ML
Optimization-Based Path Planning for Industrial Manipulators: Experimental Validation in Additive Manufacturing
arXiv cs.RO0 views
arXiv:2611.13983v1 Announce Type: new Abstract: Robotic additive manufacturing requires joint paths that reconcile robot feasibility with process-specific geometry. We develop an offline optimization-based path planner that combines joint-path regularity and self-collision penalties with process costs and constraints, so that a new process enters through its own reference, residuals, and tolerances. Full-path and boundary-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 tracking recorded during execution.
Source：https://arxiv.org/abs/2611.13983PreviousReACT: Reflection-Consistent Modulation and Cross-Load Consistency for Humanoid Velocity Tracking Under Asymmetric LoadsNextLaST0.5: A Generalist World Action Model with Interaction-Centric Latent Reasoning
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