ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems

2024-11-21

arXiv.org

AI Summary

To address prolonged motion execution time and collision risks in dual-arm robotic manipulation under relative pose constraints (e.g., collaborative scanning of unknown objects), this paper proposes a cooperative inverse kinematics framework that directly optimizes actual execution time while explicitly modeling implicit collision constraints. Departing from conventional optimization paradigms relying on surrogate metrics (e.g., joint configuration distance), we formulate a nonlinear optimization problem integrating a neural-network-based execution time approximator and implicit collision constraints, jointly resolving redundancy across both arms. To our knowledge, this is the first approach to unify physical execution time minimization and guaranteed collision-free motion within the inverse kinematics objective. Experiments on a UR5 + KUKA iiwa platform demonstrate significant reduction in task execution time, improved trajectory efficiency, preserved end-effector positioning accuracy, and full compliance with motion safety requirements throughout execution.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of both arms, specifically in tasks where only the relative pose of the robots is constrained, such as dual-arm scanning of unknown objects. Unlike traditional inverse kinematics methods that use surrogate metrics such as joint configuration distance, our method incorporates direct motion execution time and implicit collisions into the optimization process, thereby finding target joints that allow subsequent trajectory generation to get more efficient and collision-free motion. A neural network based execution time approximator is employed to predict time-efficient joint configurations while accounting for potential collisions. Through experimental evaluation on a system composed of a UR5 and a KUKA iiwa robot, we demonstrate significant reductions in execution time. The proposed method outperforms conventional approaches, showing improved motion efficiency without sacrificing positioning accuracy. These results highlight the potential of ETA-IK to improve the performance of dual-arm systems in applications, where efficiency and safety are paramount.

Problem

• Optimizing motion execution time for dual-arm robotic systems
• Incorporating direct motion time and collision avoidance in kinematics
• Enhancing efficiency and safety in constrained relative pose tasks

Innovation

• Execution-time-aware inverse kinematics for dual-arm systems
• Neural network predicts time-efficient collision-free configurations
• Direct motion time optimization outperforms traditional surrogate metrics