Tracks, wheels, and legs are all useful locomotion modes for Unmanned Ground Vehicles (UGVs), and ground robots that combine these mechanisms have the potential to climb over large obstacles. As robot morphologies include more degrees of freedom and obstacles become increasingly large and complex, UGVs will need to rely on automatic motion planning to compute the joint trajectories for traversal. This article presents a trajectory optimization formulation for multibody UGVs with combined wheel-leg and track-leg designs. We derive the dynamics and constraints for rolling wheels and circulating elliptical tracks. Using direct collocation, we formulate a model-based trajectory optimization where all constraints and objectives are written in closed-form with smooth and exact derivatives for tractable computation times with existing large-scale nonlinear optimization solvers (<1 minute). We demonstrate the trajectory optimization on numerous simulated planar wheel-leg and track-leg morphologies completing locomotion tasks, demonstrating full body dynamic coupling for the multibody system. Future work will extend this formulation to 3D and include contact planning.

Out in the field, bipedal robots need to travel on terrain that is uneven, non-rigid, and sometimes moving beneath their feet. We present a force-based double support balancing controller for such dynamic terrain scenarios for bipedal robots, and test it on the robotic bipedal platform "Tallahassee Cassie." The presented controller relies on minimal information about the robot model, requiring its kinematics and overall weight, but not inertias of individual links or components. The controller is pelvis-centric, commanding pelvis positions in Cartesian space, which a model-free PD controller converts to motor torques in joint space. By commanding forces, torques, and a frontal center of pressure in this fashion, Tallahassee Cassie is capable of balancing on a variety of scenarios, from a lifting/sliding platform, to soft foam, to a sudden drop. These results show the potential for bipedal control to balance successfully despite minimal model information, the presence of large dynamic impacts-e.g., falling through trap door, and soft series-spring deflections. These results motivate future work for walking and running controllers on dynamic terrain with relatively low reliance on modeling information.