Highly-Dynamic Movements of a Humanoid Robot Using Whole-Body Trajectory Optimization

Motion planning for legged robots is a challenging problem and remains an open area of research. Particular difficulties arise from effective underactuation, the mechanism complexity, as well as nonlinear and hybrid dynamics.
A common approach is to decompose this problem into smaller sub-problems that are solved sequentially. Recent research indicates that using a local optimal control solver, namely Differential Dynamic Programming (DDP), produces more efficient motions, with lower forces and impacts.

This master's thesis contributes in this direction by applying, evaluating and extending DDP-based whole-body trajectory optimization, pursuing three objectives. First, we develop a method for constraining DDP-like solvers in order to generate inherently balanced motion plans. Second, the proposed motion planning approach is evaluated for quasi-static and dynamic motions in a real-time physics simulation and in real-world experiments on the lightweight and biologically inspired RH5 humanoid robot. Finally, the limits of the approach and the system design are examined by solving highly-dynamic movements.