[PDF] Path Following Control System For A Small Autonomous Vehicle eBook

Author : Levent Guvenc
Publisher : John Wiley & Sons
Page : 256 pages
File Size : 25,18 MB
Release : 2021-12-06
Category : Technology & Engineering
ISBN : 1119747961

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Discover the latest research in path planning and robust path tracking control In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models Practical discussions of path generation and path modeling available in current literature In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, Autonomous Road Vehicle Path Planning and Tracking Control is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

Author : Anmol Singh Sidhu
Publisher :
Page : 144 pages
File Size : 37,35 MB
Release : 2006
Category : Motor vehicles
ISBN :

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Abstract: Autonomous vehicles capable of following a trajectory are of practical interest. The futuristic realization of such technology could be automated highways but even today this capability offers a lot of potential for application in the area of vehicle dynamics testing. Steering control is an important aspect of any autonomous vehicle. This project aims at developing and implementing a path following algorithm on a road vehicle. The objective is that the vehicle should be able to steer itself on a predefined path. The emphasis is only on lateral dynamics of the vehicle. The application of such a steering controller is to replace a human driver in vehicle dynamics testing. A well designed machine promises of being repeatable, consistent and more reliable than a human driver. The algorithm proposed in this work is suitable for implementation for testing in a controlled environment. It includes flat paved surface, open skies for visibility of satellites, etc. This thesis discusses the concept with the help of mathematical models and simulations. The algorithm is also tested with Ford F 150 at low speed.

Author : Fabian Russell Pfitz
Publisher : Logos Verlag Berlin GmbH
Page : 160 pages
File Size : 34,80 MB
Release : 2023-08-21
Category :
ISBN : 3832557059

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In this thesis, we present novel model predictive control (MPC) formulations based on a convex open-loop optimal control problem to tackle the problem setup of trajectory tracking and path following as well as the control of systems with unknown system dynamic. In particular, we consider the framework of relaxed barrier function based MPC (rbMPC). We extend the existing stability theory to the trajectory tracking and the path following problem. We establish important system theoretic properties like closed-loop stability and exact constraint satisfaction under suitable assumptions. Moreover, we evaluate the developed MPC algorithms in the area of automated driving in simulations as well as in a real-world driving scenario. Further, we consider the control of completely unknown systems based on online optimization. We divide the overall problem into the design of an estimation algorithm and a control algorithm. The control algorithm is a model-independent receding horizon control algorithm in which important system theoretic properties like convergence to the origin are guaranteed without the knowledge of the true system parameters. The estimation and control algorithm are combined together and convergence to the origin of the closed-loop system for fully unknown linear time-invariant discrete-time systems is shown.

Author : Haoan Wang (Ph. D. in electrical and computer engineering)
Publisher :
Page : 168 pages
File Size : 43,1 MB
Release : 2018
Category : Automated vehicles
ISBN :

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Safety is another significant consideration during autonomous vehicle path following. When operating the vehicle in urban area where the Vulnerable Road Users (VRUs) consisting of pedestrians and cyclists, it is necessary to respect the social space of VRUs. This dissertation proposes elastic band theory based socially acceptable collision avoidance algorithm. Results demonstrate that the vehicle tries to avoid both stationary and moving VRUs and follows the socially acceptable collision-free path successfully.

Author : Oskar Ljungqvist
Publisher : Linköping University Electronic Press
Page : 78 pages
File Size : 43,78 MB
Release : 2019-01-22
Category :
ISBN : 9176851303

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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.

Author : Adnan Tahirovic
Publisher : Springer Science & Business Media
Page : 64 pages
File Size : 34,81 MB
Release : 2013-04-18
Category : Technology & Engineering
ISBN : 144715049X

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Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.

Author : Micah DeLattre
Publisher :
Page : 0 pages
File Size : 35,43 MB
Release : 2023
Category :
ISBN :

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This thesis investigates high-speed off-road autonomous path-following using an instrumented small-scale test vehicle. The proposed test vehicle employs a combination of sensors, including GPS and rotary encoders to accurately track the vehicle's position, orientation, and speed during traversals of an off-road course. An onboard microcontroller collects and stores this sensor data in real-time which can be post-processed later. Steering and throttle limiting algorithms are implemented onto the vehicles onboard microcontroller to enable steering and throttle control. A Simulink trajectory following model was utilized in this work to simulate the vehicles traversal of a provided trajectory. The results of the simulation allow algorithm selection and controller tuning to provide the necessary steering angles and velocities to enable path following. At Penn State's test track, an off-road area was chosen as the test course for off-road driving experiments in future work. This test course was designed to feature numerous switchbacks, a straight-away where maximum speed can be achieved, steep inclined hills, rapid changes of elevation, and varying terrain. These features increase the complexity and detail needed in a proper path-following algorithm. This research discussion concludes with discussion of next steps for field testing. The overall goals are to contribute to the development of autonomous off-road vehicles for use in a range of applications, including agriculture, mining, and search and rescue. The findings of this thesis have implications for the advancement of autonomous systems technology in challenging off-road environments.

Author : Daniel Watzenig
Publisher : Springer
Page : 619 pages
File Size : 29,46 MB
Release : 2016-09-23
Category : Technology & Engineering
ISBN : 3319318950

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The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.

Author : Sheng Zhu (Mechanical engineer)
Publisher :
Page : 198 pages
File Size : 13,79 MB
Release : 2020
Category : Automated vehicles
ISBN :

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Autonomous driving is gaining popularity in research interest and industry investment over the last decade, due to its potential to increase driving safety to avoid driver errors which account for over 90% of all motor vehicle crashes. It could also help to improve public mobility especially for the disabled, and to boost the productivity due to enlarged traffic capacity and accelerated traffic flows. The path planning and following control, as the two essential modules for autonomous driving, still face critical challenges in implementations in a dynamically changing driving environment. For the local path/trajectory planning, multifold requirements need to be satisfied including reactivity to avoid collision with other objects, smooth curvature variation for passenger comfort, feasibility in terms of vehicle control, and the computation efficiency for real-time implementations. The feedback control is required afterward to accurately follow the planned path or trajectory by deciding appropriate actuator inputs, and favors smooth control variations to avoid sudden jerks. The control may also subject to instability or performance deterioration due to continuously changing operating conditions along with the model uncertainties. The dissertation contributes by raising the framework of path planning and control to address these challenges. Local on-road path planning methods from two-dimensional (2D) geometric path to the model-based state trajectory is explored. The latter one is emphasized due to its advantages in considering the vehicle model, state and control constraints to ensure dynamic feasibility. The real-time simulation is made possible with the adoption of control parameterization and lookup tables to reduce computation cost, with scenarios showing its smooth planning and the reactivity in collision avoidance with other traffic agents. The dissertation also explores both robust gain-scheduling law and model predictive control (MPC) for path following. The parameter-space approach is introduced in the former with validated robust performance under the uncertainty of vehicle load, speed and tire saturation parameter through hardware-in-the-loop and vehicle experiments. The focus is also put on improving the safety of the intended functionality (SOTIF) to account for the potential risks caused by lack of situational awareness in the absence of a system failure. Such safety hazards include the functional inability to comprehend the situation and the insufficient robustness to diverse conditions. The dissertation enhanced the SOTIF with parameter estimation through sensor fusion to increase the vehicle situational awareness of its internal and external conditions, such as the road friction coefficient. The estimated road friction coefficient helps in planning a dynamically feasible trajectory under adverse road condition. The integration of vehicle stability control with autonomous driving functions is also explored in the case that the road friction coefficient estimation is not responsive due to insufficiency in time and excitations.