Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Sathya, Ajay Suresha; Sopasakis, Pantelis; Parys, Ruben Van; Themelis, Andreas; Pipeleers, Goele; Patrinos, Panagiotis

doi:10.23919/ecc.2018.8550253

Cited by 94 publications

(86 citation statements)

References 25 publications

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“…Another approach is based on the separating hyperplane theorem (Boyd and Vandenberghe, 2004), and allows for the separation of a convex motion system and convex obstacles, or between convex motion systems, as illustrated by (Debrouwere et al, 2013) and (Mercy et al, 2017). Recently, Sathya et al (2018) have proposed a novel constraint formulation to incorporate general obstacle shapes, described as the intersection of a set of nonlinear inequalities, in the optimization problem. This paper embeds the obstacle constraint formulation presented by Sathya et al (2018) in a penalty method framework to calculate a trajectory while satisfying collision-avoidance constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Sathya et al (2018) have proposed a novel constraint formulation to incorporate general obstacle shapes, described as the intersection of a set of nonlinear inequalities, in the optimization problem. This paper embeds the obstacle constraint formulation presented by Sathya et al (2018) in a penalty method framework to calculate a trajectory while satisfying collision-avoidance constraints. The penalty parameters allow for a trade-off between the optimality of the trajectory and the extent to which the obstacle constraints may be violated.…”

Section: Introductionmentioning

confidence: 99%

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A Penalty Method Based Approach for Autonomous Navigation using Nonlinear Model Predictive Control

2018

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This paper presents a novel model predictive control strategy for controlling autonomous motion systems moving through an environment with obstacles of general shape. In order to solve such a generic non-convex optimization problem and find a feasible trajectory that reaches the destination, the approach employs a quadratic penalty method to enforce the obstacle avoidance constraints, and several heuristics to bypass local minima behind an obstacle. The quadratic penalty method itself aids in avoiding such local minima by gradually finding a path around the obstacle as the penalty factors are successively increased. The inner optimization problems are solved in real time using the proximal averaged Newton-type method for optimal control (PANOC), a first-order method which exhibits low runtime and is suited for embedded applications. The method is validated by extensive numerical simulations and shown to outperform state-of-the-art solvers in runtime and robustness. ⋆ This work benefits from KU Leuven-BOF PFV/10/002 Centre of Excellence: Optimization in Engineering (OPTEC), from the project G0C4515N of the Research Foundation-Flanders (FWO-Flanders), from Flanders Make ICON project: Avoidance of collisions and obstacles in narrow lanes, and from the KU Leuven Research project C14/15/067: B-spline based certificates of positivity with applications in engineering, from KU Leuven internal funding: StG/15/043, from Fonds de la Recherche Scientifique -FNRS and the Fonds Wetenschappelijk Onderzoek -Vlaanderen under EOS Project no 30468160 (SeLMA) and from FWO projects G086318N, G086518N.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Penalty Method Based Approach for Autonomous Navigation using Nonlinear Model Predictive Control

2018

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“…The NMPC optimization problem is solved by using PANOC [16], [17] -a recently proposed algorithm for non-convex optimization problems, which is suitable for embedded NMPC, as it requires only simple and cheap linear operations (mainly inner products of vectors) and exhibits a fast convergence. Unlike SQP, PANOC is matrix-free and only requires the computation of Jacobian-vector products, which can be computed very efficiently by backward (ad-joint) automatic differentiation.…”

Section: B Contributionsmentioning

confidence: 99%

“…PANOC is shown in Algorithm 1. L-BFGS uses a buffer of length µ of vectors s ν =ū ν+1 −ū ν and y ν = R γ (ū ν+1 )− R γ (ū ν ) to compute the update directions d ν [16], [24,Sec. 7.2].…”

Section: Fast Online Nonlinear Mpc Using Panocmentioning

confidence: 99%

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Small

Sopasakis

Fresk

et al. 2019

2019 18th European Control Conference (ECC)

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We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20 Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.

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“…Moreover, we introduce a methodology to embed conditional constraints into the motion planning problem, such as waiting at the stopping line if safe intersection crossing is impossible. Finally, we propose to exploit the proximal averaged Newton-type method for optimal control (PANOC) [8], [9] to solve the resulting nonconvex NLP in real-time.…”

Section: A Main Contribution and Outlinementioning

confidence: 99%

Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC

Katriniok

Sopasakis

Schuurmans

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion planning scheme under reasonable vehicleto-vehicle communication requirements. Each agent solves a nonlinear model predictive control problem in real time and transmits its planned trajectory to other agents, which may have conflicting objectives. The problem formulation is augmented with conditional constraints that enable the agents to decide whether to wait at a stopping line, if safe crossing is not possible. The involved nonconvex problems are solved very efficiently using the proximal averaged Newton method for optimal control (PANOC). We demonstrate the efficiency of the proposed approach in a realistic intersection crossing scenario. A. Katriniok is with Ford Research

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Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Cited by 94 publications

References 25 publications

A Penalty Method Based Approach for Autonomous Navigation using Nonlinear Model Predictive Control

A Penalty Method Based Approach for Autonomous Navigation using Nonlinear Model Predictive Control

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC

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