Abstract:
Many critical robotics applications require robustness to disturbances arising from unplanned forces, state uncertainty, and model errors. Motion planning algorithms that explicitly reason about robustness require a coupling of trajectory optimization and feedback design, where the system's closed-loop response to disturbances is optimized. Due to the often-heavy computational demands of solving such problems, the practical application of robust trajectory optimization in robotics has so far been limited. Motivated by recent work on sums-of-squares verification methods for nonlinear systems, we derive a scalable robust trajectory optimization algorithm that optimizes approximate invariant funnels along the trajectory while planning. For the case of ellipsoidal disturbance sets and LQR feedback controllers, the state and input deviations along a nominal trajectory can be computed locally in closed form, permitting fast evaluation of robust cost and constraint functions and their derivatives. The resulting algorithm is a scalable extension of classical direct transcription that demonstrably improves tracking performance over non-robust formulations while incurring only a modest increase in computational cost. We evaluate the algorithm in several simulated robot control tasks.
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