构建双臂机器人避障最优轨迹库，支持复杂协同操作。
TOCALib: Optimal control library with interpolation for bimanual manipulation and obstacles avoidance
- 基于FROST框架融合动力学约束，用DCOL方法符号化评估碰撞。
- 在Mobile Aloha上实现复杂双臂操作，轨迹优化精度高且实时性好。
- 适用于双臂机器人、双足步态控制及机器学习训练数据生成。
本文提出一种构建双臂机器人最优轨迹库的新方法——双臂最优控制与避障库（TOCALib）。该方法在FROST框架下综合考虑运动学、动力学及其他约束条件。其创新之处在于采用DCOL方法对碰撞进行符号化建模，可生成用于梯度优化的碰撞检测表达式。所提方法成功实现了复杂双臂协同操作，以Mobile Aloha为例进行了验证。该方法可推广至其他双臂机器人系统，亦可用于双足机器人步态控制，还可为操纵任务的机器学习提供高质量训练数据。
原文摘要 · Abstract (English)
The paper presents a new approach for constructing a library of optimal trajectories for two robotic manipulators, Two-Arm Optimal Control and Avoidance Library (TOCALib). The optimisation takes into account kinodynamic and other constraints within the FROST framework. The novelty of the method lies in the consideration of collisions using the DCOL method, which allows obtaining symbolic expressions for assessing the presence of collisions and using them in gradient-based optimization control methods. The proposed approach allowed the implementation of complex bimanual manipulations. In this paper we used Mobile Aloha as an example of TOCALib application. The approach can be extended to other bimanual robots, as well as to gait control of bipedal robots. It can also be used to construct training data for machine learning tasks for manipulation.
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