Dynamics, state estimation, and trajectory optimization on a kangaroo robot

The project attempts to improve the old hopping robot, by examining the actual physical structure of kangaroo. The robot adds an additional degree of freedom which is a sliding mass attached to the tail, so that the robot has the capability of adjusting the center of mass and the inertia relative to the overall system. On the aspect of simulation model, the project extends the simple passive model (RSLIP) to an eccentric and multiple parts system, including body, tail and feet. Due to the displacement of overall system’s center of mass, the model has very high nonlinearity. The highly coupled variables are difficult to analyze separately, thus the project using an indirect way to get the trajectory by optimizing the cost function. Though the simulation runs a relative complex model, the difference between the real system and the model can be seen under the experiment. Therefore, the project further proposes two methods to modify the trajectory, including a touchdown based trajectory adjustment and a dynamic trajectory selection. In order to meet the requirement of the method proposed, a predicted trajectory Gaussian filter was implemented to reduce the white noise of the sensor, and a hybrid Kalman filter architectures with fixed angular velocity model for more precise estimation. In addition to the improvement of the robot mentioned above, the thesis also proposes a posture estimator which is based on optical flow algorithm. Though this estimator has not been used in the project, it provides a scenario that robot can use dynamic visual feedback for control in the future.