IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base

TL;DR

This paper introduces a method to generate effective initial guesses for inverse kinematics in human-like dual-arm robots with mobile bases, improving IK solution success rates by optimizing a manipulability-based goodness measure.

Contribution

It proposes a novel approach using genetic algorithms and reachability maps to generate better initial guesses for IK solving in size-constrained, human-like robots.

Findings

Improved IK solution success rate with optimized initial guesses.

Effective use of reachability maps for initial guess enumeration.

Quantitative validation demonstrating increased IK solvability.

Abstract

Robots are strongly expected as a means of replacing human tasks. If a robot has a human-like physicality, the possibility of replacing human tasks increases. In the case of household service robots, it is desirable for them to be on a human-like size so that they do not become excessively large in order to coexist with humans in their operating environment. However, robots with size limitations tend to have difficulty solving inverse kinematics (IK) due to mechanical limitations, such as joint angle limitations. Conversely, if the difficulty coming from this limitation could be mitigated, one can expect that the use of such robots becomes more valuable. In numerical IK solver, which is commonly used for robots with higher degrees-of-freedom (DOF), the solvability of IK depends on the initial guess given to the solver. Thus, this paper proposes a method for generating a good initial guess for a numerical IK solver given the target hand configuration. For the purpose, we define the goodness of an initial guess using the scaled Jacobian matrix, which can calculate the manipulability index considering the joint limits. These two factors are related to the difficulty of solving IK. We generate the initial guess by optimizing the goodness using the genetic algorithm (GA). To enumerate much possible IK solutions, we use the reachability map that represents the reachable area of the robot hand in the arm-base coordinate system. We conduct quantitative evaluation and prove that using an initial guess that is judged to be better using the goodness value increases the probability that IK is solved. Finally, as an application of the proposed method, we show that by generating good initial guesses for IK a robot actually achieves three typical scenarios.