论文标题
使用全身控制和控制屏障功能的全身控制避免人类自我碰撞
Humanoid Self-Collision Avoidance Using Whole-Body Control with Control Barrier Functions
论文作者
论文摘要
这项工作将控制屏障功能（CBF）与全身控制器结合在一起，以使MIT人类生物避免自我挑战。现有的反应性控制器进行自我挑战避免，不能保证无碰撞的轨迹，因为它们不利用机器人的完整动态，从而损害了运动学的可行性。相比之下，所提出的CBF-WBC控制器可以实时推荐机器人的动力学不足，以确保无碰撞运动。在模拟中验证了这种方法的有效性。首先，一个简单的手段实验表明，CBF-WBC使机器人的手能够偏离不可行的参考轨迹，以避免自我收集。其次，将CBF-WBC与设计用于动态运动的线性模型预测控制器（LMPC）结合使用，并使用CBF-WBC来跟踪LMPC预测。步行实验表明，当高级规划师提供的脚步位置或秋千轨迹对于真正的机器人而言是不可行的，并且会产生可行的手臂运动以改善干扰恢复时，添加CBF会避免腿部自我填充。
This work combines control barrier functions (CBFs) with a whole-body controller to enable self-collision avoidance for the MIT Humanoid. Existing reactive controllers for self-collision avoidance cannot guarantee collision-free trajectories as they do not leverage the robot's full dynamics, thus compromising kinematic feasibility. In comparison, the proposed CBF-WBC controller can reason about the robot's underactuated dynamics in real-time to guarantee collision-free motions. The effectiveness of this approach is validated in simulation. First, a simple hand-reaching experiment shows that the CBF-WBC enables the robot's hand to deviate from an infeasible reference trajectory to avoid self-collisions. Second, the CBF-WBC is combined with a linear model predictive controller (LMPC) designed for dynamic locomotion, and the CBF-WBC is used to track the LMPC predictions. Walking experiments show that adding CBFs avoids leg self-collisions when the footstep location or swing trajectory provided by the high-level planner are infeasible for the real robot, and generates feasible arm motions that improve disturbance recovery.
