Efficient trajectory planning for a 4-DOF robotic arm with curve interpolation and Gaussian process inference for pick-and-place manipulation tasks

Abstract This paper presents an efficient trajectory planning method for a 4-DOF robotic arm designed for pick-and-place manipulation tasks. The method addresses several challenges, where traditional optimization approaches struggle with high dimensionality, and data-driven methods are costly to collect enough data. The proposed approach leverages Bézier curves for computationally efficient, smooth trajectory generation, minimizing abrupt changes in motion. When continuous solutions for the end-effector angle are unavailable, joint angles are interpolated using Bézier or Hermite interpolation. Additionally, we use custom metrics to evaluate deviation between the interpolated trajectory and the original trajectory, as well as the overall smoothness of the path. When a continuous solution exists, the trajectory is treated as a Gaussian process, where a prior factor is generated using the centerline. This prior is then combined with a smoothness factor to optimize the trajectory, ensuring it remains as smooth as possible within the feasible solution space through stochastic gradient descent. The method is evaluated through simulations in Nvidia Isaac Sim; results highlight the method’s suitability, and future work will explore enhancements in prior trajectory integration and smoothing techniques.