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  • 1.
    Andersson, Olov
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Tiger, Mattias
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance2018In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper (Refereed)
    Abstract [en]

    A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

  • 2.
    Bergman, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Improved Optimization of Motion Primitives for Motion Planning in State Lattices2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a framework for generating motion primitives for lattice-based motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the end-point boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.

  • 3.
    Evestedt, Niclas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach2016In: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1156-1161Conference paper (Refereed)
    Abstract [en]

    In this paper a cascaded approach for stabilizationand path tracking of a general 2-trailer vehicle configurationwith an off-axle hitching is presented. A low level LinearQuadratic controller is used for stabilization of the internalangles while a pure pursuit path tracking controller is used ona higher level to handle the path tracking. Piecewise linearityis the only requirement on the control reference which makesthe design of reference paths very general. A Graphical UserInterface is designed to make it easy for a user to design controlreferences for complex manoeuvres given some representationof the surroundings. The approach is demonstrated with challengingpath following scenarios both in simulation and on asmall scale test platform.

  • 4.
    Ling, Gustav
    et al.
    Scania CV, Södertälje, Sweden.
    Lindsten, Klas
    Scania CV, Södertälje, Sweden.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Löfberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Norén, Christoffer
    Scania CV, Södertälje, Sweden.
    Larsson, Christian A.
    Scania CV, Södertälje, Sweden.
    Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks2018In: 2018 American Control Conference (ACC), IEEE, 2018, p. 3994-4001Conference paper (Refereed)
    Abstract [en]

    The demand for fuel-efficient transport solutions are steadily increasing with the goal of reducing environmental impact and increasing efficiency. Heavy-Duty Vehicle (HDV) platooning is a promising concept where multiple HDVs drive together in a convoy with small intervehicular spacing. By doing this, the aerodynamic drag is reduced which in turn lowers fuel consumption. We propose a novel Model Predictive Control (MPC) framework for longitudinal control of the follower vehicle in a platoon consisting of two HDVs when no vehicle-to-vehicle communication is available. In the framework, the preceding vehicle's velocity profile is predicted using artificial neural networks which uses a topographic map of the road as input and is trained offline using synthetic data. The gear shifting and mass of consumed fuel for the controlled follower vehicle is modeled and used within the MPC controller. The efficiency of the proposed framework is verified in simulation examples and is benchmarked with a currently available control solution.  

  • 5.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    On motion planning and control for truck and trailer systems2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems.

    First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle.

    Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques.

    Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero.

    Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.

    List of papers
    1. Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach
    Open this publication in new window or tab >>Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach
    2016 (English)In: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1156-1161Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper a cascaded approach for stabilizationand path tracking of a general 2-trailer vehicle configurationwith an off-axle hitching is presented. A low level LinearQuadratic controller is used for stabilization of the internalangles while a pure pursuit path tracking controller is used ona higher level to handle the path tracking. Piecewise linearityis the only requirement on the control reference which makesthe design of reference paths very general. A Graphical UserInterface is designed to make it easy for a user to design controlreferences for complex manoeuvres given some representationof the surroundings. The approach is demonstrated with challengingpath following scenarios both in simulation and on asmall scale test platform.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2016
    Keywords
    cascade control, control system synthesis, graphical user interfaces, linear quadratic control, mobile robot, path planning, piecewise linear techniques
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-130950 (URN)10.1109/IVS.2016.7535535 (DOI)000390845600183 ()978-1-5090-1821-5 (ISBN)978-1-5090-1822-2 (ISBN)
    Conference
    2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016
    Projects
    iQMatic
    Funder
    VINNOVA
    Available from: 2016-09-01 Created: 2016-09-01 Last updated: 2019-01-17Bibliographically approved
    2. Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT
    Open this publication in new window or tab >>Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT
    2016 (English)In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3690-3697Conference paper, Published paper (Refereed)
    Abstract [en]

    Reversing with a dolly steered trailer configura- tion is a hard task for any driver without extensive training. In this work we present a motion planning and control framework that can be used to automatically plan and execute complicated manoeuvres. The unstable dynamics of the reversing general 2- trailer configuration with off-axle hitching is first stabilised by an LQ-controller and then a pure pursuit path tracker is used on a higher level giving a cascaded controller that can track piecewise linear reference paths. This controller together with a kinematic model of the trailer configuration is then used for forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate motion plans that are not only kinematically feasible but also include the limitations of the controller’s tracking performance when reversing. The approach is evaluated over a series of Monte Carlo simulations on three different scenarios and impressive success rates are achieved. Finally the approach is successfully tested on a small scale test platform where the motion plan is calculated and then sent to the platform for execution. 

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2016
    Series
    Intelligent Robots and Systems, E-ISSN 2153-0866 ; 2016
    National Category
    Robotics Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-134035 (URN)10.1109/IROS.2016.7759544 (DOI)000391921703107 ()9781509037629 (ISBN)9781509037612 (ISBN)9781509037636 (ISBN)
    Conference
    2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, October 9-14, 2016
    Projects
    iQMatic
    Note

    Funding agencies: FFI/VINNOVA

    Available from: 2017-01-19 Created: 2017-01-19 Last updated: 2019-01-17Bibliographically approved
    3. Path following control for a reversing general 2-trailer system
    Open this publication in new window or tab >>Path following control for a reversing general 2-trailer system
    2016 (English)In: 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2016, p. 2455-2461Conference paper, Published paper (Refereed)
    Abstract [en]

    In order to meet the requirements for autonomous systems in real world applications, reliable path following controllers have to be designed to execute planned paths despite the existence of disturbances and model errors. In this paper we propose a Linear Quadratic controller for stabilizing a 2-trailer system with possible off-axle hitching around preplanned paths in backward motion. The controller design is based on a kinematic model of a general 2-trailer system including the possibility for off-axle hitching. Closed-loop stability is proved around a set of paths, typically chosen to represent the possible output from the path planner, using theory from linear differential inclusions. Using convex optimization tools a single quadratic Lyapunov function is computed for the entire set of paths.

    Place, publisher, year, edition, pages
    IEEE, 2016
    Series
    IEEE Conference on Decision and Control, ISSN 0743-1546
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-138327 (URN)10.1109/CDC.2016.7798630 (DOI)000400048102102 ()978-1-5090-1837-6 (ISBN)
    Conference
    55th IEEE Conference on Decision and Control (CDC)
    Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2019-01-17
    4. On stability for state-lattice trajectory tracking control
    Open this publication in new window or tab >>On stability for state-lattice trajectory tracking control
    2018 (English)In: 2018 Annual American Control Conference (ACC), IEEE, 2018, p. 5868-5875Conference paper, Published paper (Refereed)
    Abstract [en]

    In order to guarantee that a self-driving vehicle is behaving as expected, stability of the closed-loop system needs to be rigorously analyzed. The key components for the lowest levels of control in self-driving vehicles are the controlled vehicle, the low-level controller and the local planner.The local planner that is considered in this work constructs a feasible trajectory by combining a finite number of precomputed motions. When this local planner is considered, we show that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, we propose a novel method for analyzing the behavior of the tracking error, how to design the low-level controller and how to potentially impose constraints on the local planner, in order to guarantee that the tracking error is bounded and decays towards zero. The proposed method is applied on a truck and trailer system and the results are illustrated in two simulation examples.

    Place, publisher, year, edition, pages
    IEEE, 2018
    Series
    American Control Conference (ACC), E-ISSN 2378-5861
    National Category
    Control Engineering Robotics
    Identifiers
    urn:nbn:se:liu:diva-152455 (URN)10.23919/ACC.2018.8430822 (DOI)978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
    Conference
    2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA
    Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-01-17
  • 6.
    Ljungqvist, Oskar
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Löfberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    On stability for state-lattice trajectory tracking control2018In: 2018 Annual American Control Conference (ACC), IEEE, 2018, p. 5868-5875Conference paper (Refereed)
    Abstract [en]

    In order to guarantee that a self-driving vehicle is behaving as expected, stability of the closed-loop system needs to be rigorously analyzed. The key components for the lowest levels of control in self-driving vehicles are the controlled vehicle, the low-level controller and the local planner.The local planner that is considered in this work constructs a feasible trajectory by combining a finite number of precomputed motions. When this local planner is considered, we show that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, we propose a novel method for analyzing the behavior of the tracking error, how to design the low-level controller and how to potentially impose constraints on the local planner, in order to guarantee that the tracking error is bounded and decays towards zero. The proposed method is applied on a truck and trailer system and the results are illustrated in two simulation examples.

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