Practical bipedal walking control on uneven terrain using surface learning and push recovery

Abstract

The design of a robotic manipulator, including the type of joints, actuators, and other geometric parameters significantly affects its precision (or positioning uncertainty) at the end-effector. Furthermore, sensor and actuator resolution and choice of control scheme will also contribute to the manipulator's precision. Modeling and simulation of these uncertainties can provide useful insight and serve as design guidelines for precision manipulators used in micro and nanomanufacturing. Of particular interest are assembly scenarios where the tolerance budgets are stringent and precision requirements are high, but there is little space for extensive sensor feedback due to a small work volume. In this paper, we investigate the effect of parametric uncertainties in a serial robot chain composed of prismatic or rotary “modules” on the overall positioning uncertainty at the end-effector. Two types of errors are considered: static errors due to misalignment and link parameter uncertainties, and dynamic errors due to inaccurate motion of individual links. Using common uncertainty metrics, we compare the precision of six different robot kinematic chain configurations and select the best suited ones for a generic Peg-in-Hole microassembly task.