Laser-Based Control Law For Autonomous Parallel And Perpendicular Parking

International audienceThis paper addresses the perpendicular and parallel parking problems of car-like vehicles for both forward and reverse maneuvers in one trial by extending the work presented in [1] using a multi sensor based controller with a weighted control scheme. The perception problem is discussed briefly considering a Velodyne VLP-16 and a SICK LMS151 as the sensors providing the required exteroceptive information. The results obtained from simulations and real experimentation for different parking scenarios show the validity and potential of the proposed approach. Furthermore, it is shown that, despite the need of handling several constraints for collision avoidance, the required computation time of the proposed approach is small enough to be used online

Control of autonomous multibody vehicles using artificial intelligence

The field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Tesi

Automatic Perpendicular and Diagonal Unparking Using a Multi-Sensor-Based Control Approach

International audienceThis paper explores the feasibility of a Multi-Sensor-Based Control (MSBC) approach for addressing forward nonparallel (perpendicular and diagonal) unparking problems of car-like vehicles as an alternative to classical approaches (e.g. path planning based, etc.). The results of individual cases are presented to illustrate the behavior and performance of the proposed approach as well as results from exhaustive simulations to evaluate the convergence and stability. The results presented in this work increase the versatility and validity of our MSBC approach towards a fully autonomous parking system

Multi-Sensor-Based Predictive Control for Autonomous Backward Perpendicular and Diagonal Parking

International audienceThis paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach for addressing backward nonparallel (perpendicular and diagonal) parking problems of car-like vehicles as an alternative to more classical (e.g. path planning based) approaches. The results of a few individual cases are presented to illustrate the behavior and performance of the proposed approach as well as results from exhaustive simulations to assess its convergence and stability. Indeed, preliminary results are encouraging, showing that the vehicle is able to park successfully from virtually any sensible initial position

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

SeaVipers - Computer Vision and Inertial Position Reference Sensor System (CVIPRSS)

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD (Tracking--Learning--Detection) object tracker. In this work, a \nickname ~prototype is evaluated, showing promising results as viable PRS with research, commercial, and industrial applications

Cost-effective robot for steep slope crops monitoring

This project aims to develop a low cost, simple and robust robot able to autonomously monitorcrops using simple sensors. It will be required do develop robotic sub-systems and integrate them with pre-selected mechanical components, electrical interfaces and robot systems (localization, navigation and perception) using ROS, for wine making regions and maize fields

RELATIVE CROSS TRACK ERROR CALCULATIONS IN ASABE/ISO 12188-2:2012 AND POWER/ENERGY ANALYSIS USING A 20 HP TRACTOR ON A FULLY ELECTRIC DRIVETRAIN

ASABE/ISO Standard 12188-2 provides test procedures for positioning and guidance systems in agricultural vehicles during straight and level travel. The standard provides excellent descriptions of test procedures, however it does not provide detail on methods to carry out the calculations necessary to calculate relative cross-track error (XTE), which is the primary measurement used to judge accuracy of the system. The standard was used to estimate the guidance accuracy of a relatively low-accuracy vehicle at 1.25 and 0.5 m s-1. At 1.25 m s-1, a nearest point calculation overestimated mean XTE by 0.8 cm, or 8.2%. The location sampling density was much higher with a 0.5 m s-1 travel speed, and mean XTE was only overestimated by 0.1 cm with the nearest point method. Power and energy data were recorded using a sled with a known weight to vary the drawbar force on asphalt. This will allow a comparison between the electric and conventional tractor over a range of forces applicable to a 20 HP tractor. The electric tractor was found to consume less than half the energy compared to a Kubota L5030 in a common configuration and a custom configuration to match the weight distribution of the electric tractor. Finger weeding tasks were recorded throughout the year capturing the duration and frequency of these tasks at the University of Kentucky (UK) consumer supported agriculture (CSA) farm. Power and energy data were recorded from the electric tractor while finger weeding. Diesel consumption was also recorded from a conventional tractor while finger weeding. Field data shows that the electric tractor needs approximately 0.532 kWh of energy while a conventional tractor requires approximately 1.258 kWh or energy to finger weed each row of vegetables. Conventional electric bills were compiled for the University of Kentucky CSA establishing an average monthly electric need. Historic NREL data was compiled establishing an average potential solar resource for central Kentucky. It was determined that a 15 kW photovoltaic array could meet the conventional electric needs of the UK CSA and supply the net energy allowing the electric tractor to meet the finger weeding need