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

2025-05-01

AI Summary

Numerical inverse kinematics (IK) solvers for humanoid-scale dual-arm mobile robots suffer from high failure rates due to joint angle limits and workspace constraints. Method: This paper proposes a robust IK solver leveraging high-quality initial guesses. We introduce a novel scaled Jacobian metric that jointly incorporates joint limits and manipulability as the evaluation criterion for initial guesses. Combining reachability map modeling with a genetic algorithm, our approach enables systematic enumeration of multiple IK solutions and global optimization over the solution space. Contribution/Results: The method significantly improves IK convergence success rate. It has been validated on a real robotic platform, successfully executing three representative domestic service tasks—object grasping, cooperative transport, and dexterous manipulation—under spatially constrained conditions. By ensuring reliable and efficient dual-arm motion planning in confined environments, our framework provides a scalable foundation for coordinated bimanual operation in human-centric settings.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Solving inverse kinematics for human-like robots with size limitations
• Generating optimal initial guesses for numerical IK solvers
• Enhancing IK solvability using manipulability and joint limit considerations

Innovation

• Uses genetic algorithm for IK initial guess optimization
• Employs scaled Jacobian matrix for manipulability index
• Utilizes reachability map for enumerating IK solutions