We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A relaxed barrier function is added to the optimization's cost function, leading to collision avoidance behavior without increasing the problem's computational complexity. Our holistic approach does not require any heuristics and enables legged robots to find whole-body motions in the presence of static and dynamic obstacles. We use a dynamically generated euclidean signed distance field for static collision checking. Collision checking for dynamic obstacles is modeled with moving cylinders, increasing the responsiveness to fast-moving agents. Furthermore, we include a Kalman filter motion prediction for moving obstacles into our receding horizon planning, enabling the robot to anticipate possible future collisions. Our experiments demonstrate collision-free motions on a quadrupedal robot in challenging indoor environments. The robot handles complex scenes like overhanging obstacles and dynamic agents by exploring motions at the robot's dynamic and kinematic limits.
翻译:我们提出了一个模型预测控制器(MPC),该控制器可以自动发现无碰撞的移动,同时考虑到系统动态、摩擦限制和运动限制。一个放松的屏障功能被添加到优化的成本功能中,导致避免碰撞的行为,而不会增加问题的计算复杂性。我们的整体方法不需要任何休眠术,而使脚下机械人能够在有静态和动态障碍的情况下找到全身运动。我们使用动态生成的欧洲克隆人签字的距离场进行静态碰撞检查。对动态障碍的碰撞检查以移动的气瓶为模型,提高对快速移动剂的反应能力。此外,我们还包括了卡尔曼过滤器运动预测,将障碍推进我们逐渐消失的地平线规划,使机器人能够预测未来可能发生的碰撞。我们的实验表明,在挑战室内环境的四重机械上,我们展示了无碰撞的动作。机器人会处理复杂的场景象,如在机器人的动态和动能极限上探索动作。