OpEn: Code Generation for Embedded Nonconvex Optimization

Sopasakis, Pantelis; Fresk, Emil; Patrinos, Panagiotis

doi:10.1016/j.ifacol.2020.12.071

Cited by 67 publications

(49 citation statements)

References 14 publications

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“…Although the local NLPs are nonconvex, we propose a problem reformulation that allows to guarantee convergence of the nonconvex consensus ADMM problem. We can run the algorithm in real time by adopting OpEn (Optimization Engine) (Sopasakis et al (2020)) which relies on the proximal averaged Newton method for optimal control (PANOC) (Stella et al (2017)) to solve the local NLPs fast.…”

Section: Main Contributionmentioning

confidence: 99%

“…•|k ) = 0 as an ALM-type constraint (Sopasakis et al (2020)). Instead, we apply the quadratic penalty method (PM) (Nocedal and Wright, 2006, Chap.…”

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

“…•|k ; α) which is a nonconvex C 1 continuous differentiable function and as such a smooth reformulation of φ [i] . To compute a local solution of NLP (15), we apply the open source code framework OpEn v0.6.2 (Sopasakis et al (2020)), available on github.com/alphaville/optimization-engine. In an inner loop, OpEn utilizes the proximal averaged Newton method for optimal control (PANOC) to solve NLP (15) for a fixed penalty α and fixed Lagrangian multipliers µ.…”

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

“…In an inner loop, OpEn utilizes the proximal averaged Newton method for optimal control (PANOC) to solve NLP (15) for a fixed penalty α and fixed Lagrangian multipliers µ. In an outer loop, α and µ are updated to achieve constraint satisfaction as described in Sopasakis et al (2020). In every iteration of Algorithm 1 at time step k, the solver is warm started with the solution of the previous iteration.…”

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

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Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles

Katriniok¹

2020

Preprint

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Connected automated vehicles (CAV) offer huge potential to improve the performance of automated vehicles (AV) without communication capabilities, especially in situations when the vehicles (or agents) need to be cooperative to accomplish their maneuver. Lane change maneuvers in dense traffic, e.g., are very challenging for non-connected AVs. To alleviate this problem, we propose a holistic distributed lane change control scheme for CAVs which relies on vehicle-to-vehicle communication. The originally centralized optimal control problem is embedded into a consensus-based Alternating Direction Method of Multipliers framework to solve it in a distributed receding horizon fashion. Although agent dynamics render the underlying optimal control problem nonconvex, we propose a problem reformulation that allows to derive convergence guarantees. In the distributed setting, every agent needs to solve a nonlinear program (NLP) locally. To obtain a real time solution of the local NLPs, we utilize the optimization engine OpEn which implements the proximal averaged Newton method for optimal control (PANOC). Simulation results prove the efficacy and real time capability of our approach.

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Section: Main Contributionmentioning

confidence: 99%

“…•|k ) = 0 as an ALM-type constraint (Sopasakis et al (2020)). Instead, we apply the quadratic penalty method (PM) (Nocedal and Wright, 2006, Chap.…”

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

Section: Agent Nlps: Smooth Reformulation and Solutionmentioning

confidence: 99%

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Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles

Katriniok¹

2020

Preprint

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“…The applicability of MPC is however tightly linked to the efficiency of the optimization method employed to solve each problem, as well as the capability of the computing platform at hand. Nevertheless, the outstanding computing advancements and the development of efficient tailored algorithms [6,8,12,13] have made a reliable employment of this control technology a reality, even on embedded devices.…”

Section: Introductionmentioning

confidence: 99%

Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC

Nag¹,

Huang²,

Themelis³

et al. 2022

Preprint

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We propose a model predictive control (MPC) based approach to a flock control problem with obstacle avoidance capability in a leader-follower framework, utilizing the future trajectory prediction computed by each agent. We employ the traditional Reynolds' flocking rules (cohesion, separation, and alignment) as a basis, and tailor the model to fit a navigation (as opposed to formation) purpose. In particular, we introduce several concepts such as the credibility and the importance of the gathered information from neighbors, and dynamic trade-offs between references. They are based on the observations that near-future predictions are more reliable, agents closer to leaders are implicit carriers of more educated information, and the predominance of either cohesion or alignment is dictated by the distance between the agent and its neighbors. These features are incorporated in the MPC formulation, and their advantages are discussed through numerical simulations.

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Look Before You Sweep: Visibility-Aware Motion Planning

Goretkin

Kaelbling

Lozano-Pérez

2020

Springer Proceedings in Advanced Robotics

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Robotics programming typically involves a trade-off between the ease of use offered by Python and the run-time performance of C++. While multilanguage architectures address this trade-off by coupling Python's ergonomics with C++'s speed, they introduce complexity at the language interface. This paper proposes using Julia for performance-critical tasks within Python ROS 2 applications, providing an elegant solution that streamlines the development process without disrupting the existing Python workflow.

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OpEn: Code Generation for Embedded Nonconvex Optimization

Cited by 67 publications

References 14 publications

Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles

Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles

Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC

Look Before You Sweep: Visibility-Aware Motion Planning

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