Computer Science > Robotics
[Submitted on 15 Jan 2021 (v1), last revised 26 Mar 2021 (this version, v2)]
Title:Constraint Handling in Continuous-Time DDP-Based Model Predictive Control
View PDFAbstract:The Sequential Linear Quadratic (SLQ) algorithm is a continuous-time variant 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.
Submission history
From: Jean-Pierre Sleiman [view email][v1] Fri, 15 Jan 2021 11:29:11 UTC (1,859 KB)
[v2] Fri, 26 Mar 2021 11:33:11 UTC (1,113 KB)
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