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.

翻译：本文提出ETA-IK，一种专为双臂机器人系统设计的新型执行时间感知逆运动学方法。其主要目标是通过利用双臂的冗余度来优化运动执行时间，特别是在仅约束机器人相对位姿的任务中，例如对未知物体的双臂扫描。与使用关节构型距离等替代指标的传统逆运动学方法不同，我们的方法将直接运动执行时间和隐式碰撞纳入优化过程，从而找到能使后续轨迹规划生成更高效且无碰撞运动的目标关节。我们采用基于神经网络的执行时间近似器来预测时间效率高的关节构型，同时考虑潜在的碰撞。通过在由UR5和KUKA iiwa机器人组成的系统上进行实验评估，我们证明了执行时间的显著减少。所提出的方法优于传统方法，在未牺牲定位精度的前提下提高了运动效率。这些结果突显了ETA-IK在效率与安全性至关重要的应用场景中提升双臂系统性能的潜力。