Note to Practitioners—Modern industrial automation systems demand flexible and precise robotic solutions capable of handling diverse objects in dynamic workspaces. This paper presents an NLMPC-based trajectory planning strategy tailored for multi-part, multi-location pick-and-place operations. The proposed approach supports flexible, precise, and collision-free robotic motion, making it well-suited for advanced material handling applications. Incorporating penalties on terminal velocity and acceleration leads to smoother stops, increased placement precision, and reduced mechanical wear, while applying Euclidean distance constraints ensures accurate final positioning of the end-effector. The method is computationally efficient and runs in real time on a standard CPU. Although the simulations assume known object locations, the framework is extensible to real environments with integrated perception. Open-source code and videos are provided to support adoption and replication.">

Trajectory and Flow Optimization for Multi-Part, Multi-Location Pick-and-Place Tasks Using Nonlinear Model Predictive Control (original) (raw)

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