论文标题
使用无知的卡尔曼滤波器适用于接触富含机器人系统的自适应力控制器
Adaptive Force Controller for Contact-Rich Robotic Systems using an Unscented Kalman Filter
论文作者
论文摘要
在多点接触系统中,精确的力控制对于在机器人及其环境之间实现稳定且安全的相互作用至关重要。因此,我们演示了一个具有自动调整的入学控制器,可以应用于这些系统。控制器的目标是跟踪每个接触点的目标扳手轮廓,同时考虑旋转摩擦引起的额外扭矩。我们的接收控制器在在线操作期间是自适应的,该方法在遵循用户指定的培训目标的同时调整了控制器的收益。这些目标包括促进控制器的稳定性,例如尽可能紧密地跟踪扳手配置文件,确保控制输出在最小化滑移的力极限之内,并避免诱导运动学奇异性的配置。我们使用多限制的攀岩机器人来证明控制器在硬件上的鲁棒性和运动任务。
In multi-point contact systems, precise force control is crucial for achieving stable and safe interactions between robots and their environment. Thus, we demonstrate an admittance controller with auto-tuning that can be applied for these systems. The controller's objective is to track the target wrench profiles of each contact point while considering the additional torque due to rotational friction. Our admittance controller is adaptive during online operation by using an auto-tuning method that tunes the gains of the controller while following user-specified training objectives. These objectives include facilitating controller stability, such as tracking the wrench profiles as closely as possible, ensuring control outputs are within force limits that minimize slippage, and avoiding configurations that induce kinematic singularity. We demonstrate the robustness of our controller on hardware for both manipulation and locomotion tasks using a multi-limbed climbing robot.