Incrementally Stochastic and Accelerated Gradient Information mixed Optimization for Manipulator Motion Planning

TL;DR

This paper presents iSAGO, a novel motion planning algorithm for robotic manipulators that combines stochastic, accelerated gradient methods with Bayesian inference to improve efficiency and success rate in narrow workspace environments.

Contribution

The paper introduces iSAGO, a new incremental stochastic and accelerated gradient-based optimization method for manipulator motion planning, integrating Bayesian inference for enhanced performance.

Findings

iSAGO achieves the highest success rate among tested planners.

iSAGO demonstrates moderate solving efficiency.

iSAGO outperforms five other planners in benchmarks.

Abstract

This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa in a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.