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Sampling Based Motion Planning for Heavy Duty Autonomous Vehicles
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges.

First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario.

Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results.

Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. , 60 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1762
National Category
Control Engineering Vehicle Engineering Robotics Computer Science Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-132769ISBN: 9789176856673 (Print)OAI: oai:DiVA.org:liu-132769DiVA: diva2:1049189
Presentation
2016-12-02, 10:15 (Swedish)
Supervisors
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23Bibliographically approved
List of papers
1. Sampling Recovery for Closed Loop Rapidly Expanding Random Tree using Brake Profile Regeneration
Open this publication in new window or tab >>Sampling Recovery for Closed Loop Rapidly Expanding Random Tree using Brake Profile Regeneration
2015 (English)In: Intelligent Vehicles Symposium (IV), 2015 IEEE, IEEE , 2015, 101-106 p.Conference paper (Refereed)
Abstract [en]

In this paper an extension to the sampling based motion planning framework CL-RRT is presented. The framework uses a system model and a stabilizing controller to sample the perceived environment and build a tree of possible trajectories that are evaluated for execution. Complex system models and constraints are easily handled by a forward simulation making the framework widely applicable. To increase operational safety we propose a sampling recovery scheme that performs a deterministic brake profile regeneration using collision information from the forward simulation. This greatly increases the number of safe trajectories and also reduces the number of samples that produce infeasible results. We apply the framework to a Scania G480 mining truck and evaluate the algorithm in a simple yet challenging obstacle course and show that our approach greatly increases the number of feasible paths available for execution.

Place, publisher, year, edition, pages
IEEE, 2015
Series
, IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
Keyword
RRT, Autonomous vehicles, motion planning
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-120929 (URN)10.1109/IVS.2015.7225670 (DOI)000380565800018 ()9781467372664 (ISBN)
Conference
2015 IEEE Intelligent Vehicles Symposium (IV), June 28 - July 1, 2015. COEX, Seoul, Korea
Projects
iQMatic
Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2016-11-23Bibliographically approved
2. 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, 1156-1161 p.Conference 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
Keyword
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)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: 2016-11-23Bibliographically approved

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