Implementation of Stable and Efficient Hopping with Serial Elastic Actuators

Inspired by biological systems, robots that exploit the natural dynamics of compliant joints are developed in recent years to obtain stable and efficient locomotion. In these robots, series elastic actuator (SEA) is widely used due to its compliant property and energy storage capacity. However, robots that are equipped with SEA have drawbacks of substantial delay and limited bandwidth. Additionally, high speed locomotion also engenders severe vibration and cause noise pollution in posture measurement of the robot. These inevitable features make the efficient robots hard to demonstrate precise control and perform dynamic balance. To cope with these problems, beside traditional hopping and foot hold selection algorithms, two methods are proposed in this paper for consecutive hopping: (1) a position controller which generates active damping to stabilize the joint position;(2) a learning algorithm for body balance control. The learning algorithm discretizes the continuous control problem into phases and adopts integration form of body dynamics to maintain balance. Instead of empirically tuning the control parameters, model identification and learning algorithms are employed to automatically tune these proposed controllers. Experiments were conducted on SEA based single leg robot by swinging leg between two demanded position and maintaining body balance during consecutive hopping. By combining the proposed algorithms, stable and efficient hopping was implemented.