Multisensor-Based Predictive Control for Autonomous Parking

David, Pérez-Morales,; Kermorgant, Olivier; Dominguez-Quijada, Salvador; Martinet, Philippe

doi:10.1109/tro.2021.3091285

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…It can lessen sensor limits, enhance perceptual precision, and make it possible to have a more comprehensive grasp of the world. Trajectory fusion offers the potential to improve AVs' situational awareness and decision-making abilities by incorporating data from nearby vehicles [1].…”

Section: State Of the Artmentioning

confidence: 99%

Assessment Possibilities for Trajectory Fusion of Two Vehicles in the Case of Automated Vehicles

Cordoş,

Barabás,

Todoruţ

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Because of the increasing integration of automation and cutting-edge technology into modern transportation systems, the development of efficient and dependable Automated Vehicles (AVs) has become an important area of research. One of the most important characteristics of AVs is their ability to perceive and navigate complex environments safely. The process of combining trajectory data from multiple vehicles, known as trajectory fusion, has the potential to improve AV perception. The feasibility of implementing trajectory fusion in the context of two autonomous vehicles coexisting in the same operational environment is investigated in this study. The primary goal of this research is to investigate the effectiveness of trajectory fusion in increasing the environmental awareness of Automated Vehicles (AVs), while also investigating its implications for the decision-making mechanisms that govern these vehicles. The MATLAB Simulink simulation platform was essential in carrying out this investigation. The intricate dynamics of trajectory fusion within the context of dual AVs were meticulously analyzed and evaluated using this software.

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Section: State Of the Artmentioning

confidence: 99%

Assessment Possibilities for Trajectory Fusion of Two Vehicles in the Case of Automated Vehicles

Cordoş,

Barabás,

Todoruţ

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The resulting control strategy combines the advantages of IBVS, i.e., reactivity and absence of metric localization [5], with the ones of NMPC, i.e., the possibility to take into account constraints such as joints limits and camera field of view during the minimization process. Over the last years, numerous VPC-based controllers were developed to control robotic systems such as a camera mounted on a robotic arm [6] [7] [8] [9], a quadrotor UAV [10], a mobile robot [11] [12], an autonomous underwater vehicle [13] or a tendondriven continuum robot [14]. Regarding mobile manipulators, NMPC strategies usually do not express the task in the image space.…”

Section: Introductionmentioning

confidence: 99%

Multi-camera Visual Predictive Control Strategy for Mobile Manipulators

Bildstein

Durand-Petiteville

Cadenat

2023

2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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This work aims at designing a visual predictive control (VPC) scheme for a mobile manipulator equipped with two cameras. The task consists in accurately positioning the endeffector camera while starting a few meters away from the desired pose with a tucked arm. Three challenges are addressed in this paper: the initial unavailability of the visual features, the arm singularities together with the closed-loop stability, and the final positioning accuracy. The first one is dealt with by choosing image features extracted from both cameras and by suitably switching between them, the second one is tackled through a suitable manipulability measure introduced in the cost function, and the two last ones are fulfilled via the definition of an enhanced terminal constraint. The proposed approach has been validated experimentally on TIAGo robot. The obtained results show its relevance and its efficiency.

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“…The obtained control scheme thus combines the advantages of IBVS, i.e., reactivity and absence of metric localization [5], with the ones of NMPC, i.e., ability to explicitly deal with constraints such as control inputs boundaries or camera field of view limits. Over the last decade, the interest for VPC-based controllers has grown and numerous schemes were designed to control different robotic systems: a camera mounted on a robotic arm [6] [7] [8], a flying camera [9] [10], a mobile robot [11], an autonomous underwater vehicle [12] or a tendon-driven continuum robot [13]. However, MPC strategies aiming at controlling a mobile manipulator are usually not defined in the image space.…”

Section: Introductionmentioning

confidence: 99%

Visual Predictive Control Strategy for Mobile Manipulators

Bildstein

Durand-Petiteville

Cadenat

2022

2022 European Control Conference (ECC)

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This work aims at designing a visual predictive control (VPC) scheme for a mobile manipulator. It consists in combining image-based visual servoing with model predictive control to benefit from the advantages of both control structures. Two challenges are addressed in this paper: the choice of the visual features and the closed-loop stability. The first ones rely on image moments to improve the end effector positioning precision. The second one is tackled through a terminal constraint coupled with suitable input constraints to reduce the computational burden. Simulation results using ROS and Gazebo validate the proposed approach.

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Multisensor-Based Predictive Control for Autonomous Parking

Cited by 5 publications

References 27 publications

Assessment Possibilities for Trajectory Fusion of Two Vehicles in the Case of Automated Vehicles

Assessment Possibilities for Trajectory Fusion of Two Vehicles in the Case of Automated Vehicles

Multi-camera Visual Predictive Control Strategy for Mobile Manipulators

Visual Predictive Control Strategy for Mobile Manipulators

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