Constrained Bimanual Planning with Analytic Inverse Kinematics

TL;DR

This paper introduces a novel approach for bimanual robot motion planning by leveraging an analytic inverse kinematics solution to simplify constraints and improve the feasibility of planning in complex configuration spaces.

Contribution

It presents a new parametrization of the bimanual configuration space using analytic inverse kinematics, enabling more effective planning with existing algorithms.

Findings

Reduced complexity in bimanual motion planning

Enhanced feasibility of sampling-based algorithms

Improved planning efficiency in constrained spaces

Abstract

In order for a bimanual robot to manipulate an object that is held by both hands, it must construct motion plans such that the transformation between its end effectors remains fixed. This amounts to complicated nonlinear equality constraints in the configuration space, which are difficult for trajectory optimizers. In addition, the set of feasible configurations becomes a measure zero set, which presents a challenge to sampling-based motion planners. We leverage an analytic solution to the inverse kinematics problem to parametrize the configuration space, resulting in a lower-dimensional representation where the set of valid configurations has positive measure. We describe how to use this parametrization with existing motion planning algorithms, including sampling-based approaches, trajectory optimizers, and techniques that plan through convex inner-approximations of collision-free space.