A predictive neural hierarchical framework for on-line time-optimal motion planning and control of black-box vehicle models

Piccinini, Mattia; Larcher, Matteo; Pagot, Edoardo; Piscini, Davide; Pasquato, Leone; Biral, Francesco

doi:10.1080/00423114.2022.2035776

Cited by 4 publications

(23 citation statements)

References 48 publications

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“…The development of online minimum-time motion planning and control techniques for autonomous vehicles is a complex and partly unsolved problem [1]. Simulations have been setup [2], [3], [4], [5], [6] to validate the algorithms in controlled environments, before the (often risky and expensive) experimental testing [7], [8], [9], [10], [11], [12]. The planning and control tasks become even more involved when most of the vehicle parameters are not known.…”

Section: Introductionmentioning

confidence: 99%

“…High-level tracking NMPC was used for example in [5], [9], and [12], to track online a pre-computed race-line. E-NMPC planners were employed in [2], [6], [7], and [17], yet with very simplified vehicle models. Low-level controllers were developed to track the high-level state trajectories, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Low-level controllers were developed to track the high-level state trajectories, e.g. using NMPC with more complex vehicle models [5], [9], [18], neural networks [2], [19], proportional-integral-derivative (PID) controllers [20] or other methods.…”

Section: Introductionmentioning

confidence: 99%

“…While the works cited above used a direct OC approach for both tracking and economic NMPC, our previous studies [2], [7] employed an indirect OC technique for online minimumtime E-NMPC. Interested readers can find a comparison of direct and indirect methods for (offline) minimum-time OC in [28].…”

Section: Introductionmentioning

confidence: 99%

“…In [7], we used a simplified vehicle model to control a 1:8 remote control (RC) car near the handling limits, along a circuit, modeling the nonlinear handling diagram and a G-G-v constraint. Recently, in [2] we presented a hierarchical framework for online minimum-time motion planning and control of a black-box vehicle model. An offline learning phase enabled the identification of the high-and low-level models.…”

Section: Introductionmentioning

confidence: 99%

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A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control

Piccinini,

Taddei,

Larcher

et al. 2023

IEEE Access

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This paper presents a hierarchical framework with novel analytical and neural physics-driven models, to enable the online planning and tracking of minimum-time maneuvers, for a vehicle with partiallyunknown parameters. We introduce a lateral speed prediction model for high-level motion planning with economic nonlinear model predictive control (E-NMPC). A low-level steering controller is developed with a novel feedforward-feedback physics-driven artificial neural network (NN). A longitudinal dynamic model is identified to tune a low-level speed-tracking controller. The high-and low-level control models are identified with an automatic three-step scheme, combining open-loop and closed-loop maneuvers to model the maximum acceleration G-G-v performance constraint for E-NMPC, and to capture the effect of the longitudinal acceleration on the lateral dynamics. The proposed framework is used in a simulation environment, for the online closed-loop control of a highly detailed sedan vehicle simulator, whose parameters are partiallyunknown. Two different circuits are adopted to validate the approach, and a robustness analysis is performed by varying the vehicle mass and the load distribution. A minimum-time optimal control problem is solved offline and used for a comparison with the closed-loop results. A video demonstrating both the automatic three-step identification scheme and the motion planning and control results is available at the following link: https://www.youtube.com/watch?v=xQ_T96IjGP8. INDEX TERMSAutonomous racing, model learning, model predictive control (MPC), motion planning, neural networks, trajectory optimization. Open Access funding provided by 'Università degli Studi di Trento' within the CRUI CARE Agreement

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control

Piccinini,

Taddei,

Larcher

et al. 2023

IEEE Access

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Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

Pagot,

Piccinini,

Bertolazzi

et al. 2023

IEEE Access

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This paper presents a framework to plan and execute autonomous parking maneuvers in complex parking scenarios. We formulate a minimum-time optimal control problem for trajectory planning, using an indirect optimal control approach. A novel smooth penalty function is devised for collision avoidance with optimal control, and an effective technique is adopted to compute an initial solution guess. The trajectory planning tasks are solved with low computational times, and a dense mesh is used to discretize the domain of the optimal control problems, resulting in accurate collision-free solutions. The planned parking maneuvers are tracked with an original pseudo-neural feedforward-feedback steering controller, which outperforms other techniques from the literature, and a feedback longitudinal controller, to drive a realistic 14-degree-of-freedom vehicle simulator. We validate the planning and tracking algorithms in challenging narrow parking scenarios, including reverse, parallel and angle parking, and unstructured environments. The framework robustness is assessed by changing the vehicle mass, the road adherence conditions, and by introducing measurement noise with realistic sensor models. A video of the trajectory planning and tracking results is available as supplementary material.INDEX TERMS Autonomous parking, optimal control, trajectory optimization, trajectory tracking.

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Robust and Sample-Efficient Estimation of Vehicle Lateral Velocity Using Neural Networks With Explainable Structure Informed by Kinematic Principles

Lio,

Piccinini,

Biral

2023

IEEE Trans. Intell. Transport. Syst.

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This paper presents kinematics-structured neural networks (KS-NN) for the lateral speed estimation of vehicles. The internal structure of the networks is designed to incorporate the kinematic principles, enhancing the physical explainability and generalization capacity. Both the internal structure and training method are devised for better generalization performance. Various linear and nonlinear variants of our estimator are assessed for accuracy and robustness. The approach is validated using an openly accessible dataset with two race cars. The performance of the novel networks is evaluated against Luenberger, neural network, factor graph and Kalman filter estimators from the literature. In comparison with a Luenberger kinematic observer, our networks improve noise rejection, and overcome the well-known observability problem for low yaw rates. Compared to existing neural network estimators, our networks exhibit better generalization capacity, are more sampleefficient, require fewer learnable parameters, and their structure is physically explainable.

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A predictive neural hierarchical framework for on-line time-optimal motion planning and control of black-box vehicle models

Cited by 4 publications

References 48 publications

A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control

A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control

Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

Robust and Sample-Efficient Estimation of Vehicle Lateral Velocity Using Neural Networks With Explainable Structure Informed by Kinematic Principles

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