Open access
Datum
2021Typ
- Conference Paper
ETH Bibliographie
yes
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Abstract
The Sequential Linear Quadratic (SLQ) algorithm is a continuous-time version of the well-known Differential Dynamic Programming (DDP) technique with a Gauss-Newton Hessian approximation. This family of methods has gained popularity in the robotics community due to its efficiency in solving complex trajectory optimization problems. However, one major drawback of DDP-based formulations is their inability to properly incorporate path constraints. In this paper, we address this issue by devising a constrained SLQ algorithm that handles a mixture of constraints with a previously implemented projection technique and a new augmented-Lagrangian approach. By providing an appropriate multiplier update law, and by solving a single inner and outer loop iteration, we are able to retrieve suboptimal solutions at rates suitable for real-time model-predictive control applications. We particularly focus on the inequality-constrained case, where three augmented-Lagrangian penalty functions are introduced, along with their corresponding multiplier update rules. These are then benchmarked against a relaxed log-barrier formulation in a cart-pole swing up example, an obstacle-avoidance task, and an object-pushing task with a quadrupedal mobile manipulator. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000476412Publikationsstatus
publishedExterne Links
Buchtitel
2021 IEEE International Conference on Robotics and Automation (ICRA)Seiten / Artikelnummer
Verlag
IEEEKonferenz
Thema
Optimization and Optimal Control; RSL; TenneTOrganisationseinheit
09570 - Hutter, Marco / Hutter, Marco
ETH Bibliographie
yes
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