Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

Aguiar, A. Pedro; Bayer, Florian A.; Hauser, John; Häusler, Andreas J.; Notarstefano, Giuseppe; Pascoal, António; Rucco, Alessandro; Saccon, Alessandro

doi:10.1007/978-3-319-55372-6_10

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Note that by letting δ → 0, the standard logarithmic barrier is retrieved. Furthermore, it has been shown that the optimal solution can be obtained for a nonzero value of δ [22], when the gradient of the penalty term is larger than the Lagrange multiplier of the associated constraint. Optimization with the relaxed barrier function can thus be interpreted as an augmented Lagrangian approach when h < δ and as a logbarrier method for h ≥ δ.…”

Section: B Relaxed Barrier Functionsmentioning

confidence: 99%

Feedback MPC for Torque-Controlled Legged Robots

Grandia

Farshidian

Ranftl

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

123

103

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The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.

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Section: B Relaxed Barrier Functionsmentioning

confidence: 99%

Feedback MPC for Torque-Controlled Legged Robots

Grandia

Farshidian

Ranftl

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

123

103

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“…Optimal control methods for motion planning are especially suited to take directly into account time and energy objectives, as well vehicle dynamics and ambient constraints. Direct multiple shooting [8], pseudo-spectral [17], and PRONTO [3] are but a few examples. In general, they are quite demanding in terms of computational power.…”

Section: B Multiple Amvs Motion Planningmentioning

confidence: 99%

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

Volpi

Smith

Pascoal

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

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There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research.WiMUST uses a team of cooperative autonomous marine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicating nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely automatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring intervehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.

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“…Extensions of PRONTO have been proposed for constrained optimization [30] and optimal control on Lie groups [31]. PRONTO has been applied to several contexts as motion planning of single and multiple vehicles, see, e.g., [32] and references therein. In [33] an iterative numerical method based on PRONTO is developed, where the optimal control problem is tackled via a constrainedgradient descent.…”

Section: Introductionmentioning

confidence: 99%

“…The immediate consequence is that the linearization considered when evaluating the adjoint equations is computed about the system trajectory associated to ( αk , μk ). In (32), in fact, the matrices Ãk t , Bk t , ãk t , bk t are defined as…”

mentioning

confidence: 99%

GoPRONTO: a Feedback-based Framework for Nonlinear Optimal Control

Sforni,

Spedicato,

Notarnicola

et al. 2021

Preprint

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In this paper we propose a first-order, feedback-based approach to solve nonlinear optimal control problems. Taking inspiration from Hauser's PRojection Operator Newton method for Trajectory Optimization (PRONTO), we develop Go-PRONTO, a generalized first-order framework based on a suitable embedding of the original dynamics into a closed-loop system. By exploiting this feedbackbased shooting, we are able to reinterpret the optimal control problem as the minimization of a cost function, depending on a state-input curve, whose gradient can be computed by resorting to a suitable costate equation. This convenient reformulation gives room for a collection of accelerated numerical optimal control schemes based on Conjugate gradient, Heavy-ball, and Nesterov's accelerated gradient.An interesting original feature of GoPRONTO is that it does not require to solve quadratic optimization problems, so that it is well suited for the resolution of optimal control problems involving large-scale systems. To corroborate the theoretical results, numerical simulations on the optimal control of an inverted pendulum and a train of 50 inverted pendulum-on-cart systems are shown.

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Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

Cited by 11 publications

References 29 publications

Feedback MPC for Torque-Controlled Legged Robots

Feedback MPC for Torque-Controlled Legged Robots

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

GoPRONTO: a Feedback-based Framework for Nonlinear Optimal Control

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