Real-world applications of bipedal robot walking require accurate, real-time state estimation. State estimation for locomotion over dynamic rigid surfaces (DRS), such as elevators, ships, public transport vehicles, and aircraft, remains under-explored, although state estimator designs for stationary rigid surfaces have been extensively studied. Addressing DRS locomotion in state estimation is a challenging problem mainly due to the nonlinear, hybrid nature of walking dynamics, the nonstationary surface-foot contact points, and hardware imperfections (e.g., limited availability, noise, and drift of onboard sensors). Towards solving this problem, we introduce an Invariant Extended Kalman Filter (InEKF) whose process and measurement models explicitly consider the DRS movement and hybrid walking behaviors while respectively satisfying the group-affine condition and invariant form. Due to these attractive properties, the estimation error convergence of the filter is provably guaranteed for hybrid DRS locomotion. The measurement model of the filter also exploits the holonomic constraint associated with the support-foot and surface orientations, under which the robot's yaw angle in the world becomes observable in the presence of general DRS movement. Experimental results of bipedal walking on a rocking treadmill demonstrate the proposed filter ensures the rapid error convergence and observable base yaw angle.
翻译:双层机器人行走的实际应用需要准确、实时的国家估计。国家对动态硬表面(如电梯、船舶、公共交通车辆和飞机等)的升降估计仍然未得到充分探讨,尽管对固定硬表面的国家估计显示器设计进行了广泛研究。在州一级进行DRS移动估计是一个具有挑战性的问题,主要是因为行走动态的非线性、混合性质、非静止表面-脚接触点和硬件不完善(例如,有限可用性、噪音和机上传感器漂移)。为解决这一问题,我们引入了一个不易变的卡尔曼扩展过滤器(InEKF),其过程和测量模型明确考虑到DRS运动和混合行走行为,同时分别满足了群体-情感条件和变异形式。由于这些有吸引力的特性,过滤器的估计差错趋同为混合式DRS行走动态、非静止表面-脚接触点和硬件不完善(例如,有限可用性地、噪音和机上传感器漂移)。为了解决这个问题,我们引入了一种不易变的卡尔曼过滤器扩展过滤器过滤器过滤器过滤器过滤器过滤器过滤器过滤器过滤器的制约。在总体轨道上移动轨道上的快速趋近地展示了世界轨道结果。在普通轨道上展示基础上,从而可以保证世界上快速观测结果。