Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties
| Autor: | Gokhan Alcan, Ville Kyrki |
|---|---|
| Přispěvatelé: | Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Constrained Motion Planning Mechanical Engineering Biomedical Engineering Optimization and Optimal Control Computer Science Applications Human-Computer Interaction Computer Science - Robotics Planning Under Uncertainty Artificial Intelligence Control and Systems Engineering Robot Safety Motion and Path Planning Computer Vision and Pattern Recognition Robotics (cs.RO) |
| Popis: | 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, it is challenging to ensure that the constraints are not violated. In this paper, we propose Safe-CDDP, a safe trajectory optimization and control approach 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 evaluated on three different robot dynamics with up to 12 degrees of freedom in simulation. The computational feasibility and applicability of the approach are demonstrated with a physical hardware implementation. 8 pages, 5 figures, Accepted to IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
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