19 research outputs found
Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties
Safe operation of systems such as robots requires them to plan and execute
trajectories subject to safety constraints. When those systems are subject to
uncertainties in their dynamics, ensuring that the constraints are not violated
is challenging. In this paper, we propose a safe trajectory optimization and
control approach (Safe-CDDP) for systems under additive uncertainties and
non-linear safety constraints based on constrained differential dynamic
programming (DDP). The safety of the robot during its motion is formulated as
chance-constraints with user-chosen probabilities of constraint satisfaction.
The chance constraints are transformed into deterministic ones in DDP
formulation by constraint tightening. To avoid over conservatism during
constraint tightening, linear control gains of the feedback policy derived from
the constrained DDP are used in the approximation of closed-loop uncertainty
propagation in prediction. The proposed algorithm is empirically demonstrated
on three different robot dynamics with up to 12 states and the results show the
applicability of the approach for safety-aware applications.Comment: 7 pages, 4 figures, submitted to ICRA 202