Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Performance and Extreme Conditions Analysis Based on Iterative Modelling Algorithm for Multi-Trailer AGVs

Automatic guidance vehicles (AGV) are industrial vehicles that play an important role in the development of smart manufacturing systems and Industry 4.0. To provide these autonomous systems with the flexibility that is required today in these industrial workspaces, AGV computational models are necessary in order to analyze their performance and design efficient planning and control strategies. To address these issues, in this work, the mathematical model and the algorithm that implement a computational control-oriented simulation model of a hybrid tricycle-differential AGV with multi-trailers have been developed. Physical factors, such as wheel-ground interaction and the effect of vertical and lateral loads on its dynamics, have been incorporated into the model. The model has been tested in simulation with two different controllers and three trajectories: a circumference, a square, and an s-shaped curve. Furthermore, it has been used to analyze extreme situations of slipping and capsizing and the influence of the number of trailers on the tracking error and the control effort. This way, the minimum lateral friction coefficient to avoid slipping and the minimum ratio between the lateral and height displacement of the center of gravity to avoid capsizing have been obtained. In addition, the effect of a change in the friction coefficient has also been simulated

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error

Grasping, Perching, And Visual Servoing For Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) have seen a dramatic growth in the consumer market because of their ability to provide new vantage points for aerial photography and videography. However, there is little consideration for physical interaction with the environment surrounding them. Onboard manipulators are absent, and onboard perception, if existent, is used to avoid obstacles and maintain a minimum distance from them. There are many applications, however, which would benefit greatly from aerial manipulation or flight in close proximity to structures. This work is focused on facilitating these types of close interactions between quadrotors and surrounding objects. We first explore high-speed grasping, enabling a quadrotor to quickly grasp an object while moving at a high relative velocity. Next, we discuss planning and control strategies, empowering a quadrotor to perch on vertical surfaces using a downward-facing gripper. Then, we demonstrate that such interactions can be achieved using only onboard sensors by incorporating vision-based control and vision-based planning. In particular, we show how a quadrotor can use a single camera and an Inertial Measurement Unit (IMU) to perch on a cylinder. Finally, we generalize our approach to consider objects in motion, and we present relative pose estimation and planning, enabling tracking of a moving sphere using only an onboard camera and IMU

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Vision-based trailer pose estimation for articulated vehicles

Articulated Heavy Goods Vehicles (HGVs) are more efficient than conventional rigid lorries, but exhibit reduced low-speed manoeuvrability and high-speed stability. Technologies such as autonomous reversing and path-following trailer steering can mitigate this, but practical limitations of the available sensing technologies restrict their commercialisation potential. This dissertation describes the development of practical vision-based articulation angle and trailer off-tracking sensing for HGVs. Chapter 1 provides a background and literature review, covering important vehicle technologies, existing commercial and experimental sensors for articulation angle and off-tracking measurement, and relevant vision-based technologies. This is followed by an introduction to pertinent computer vision theory and terminology in Chapter 2. Chapter 3 describes the development and simulation-based assessment of an articulation angle sensing concept. It utilises a rear-facing camera mounted behind the truck or tractor, and one of two proposed image processing methods: template-matching and Parallel Tracking and Mapping (PTAM). The PTAM-based method was shown to be the more accurate and versatile method in full-scale vehicle tests. RMS measurement errors of 0.4-1.6$^\circ$ were observed in tests on a tractor semi-trailer (Chapter 4), and 0.8-2.4$^\circ$ in tests on a Nordic combination with two articulation points (Chapter 5). The system requires no truck-trailer communication links or artificial markers, and is compatible with multiple trailer shapes, but was found to have increasing errors at higher articulation angles. Chapter 6 describes the development and simulation-based assessment of a trailer off-tracking sensing concept, which utilises a trailer-mounted stereo camera pair and visual odometry. The concept was evaluated in full-scale tests on a tractor semi-trailer combination in which camera location and stereo baseline were varied, presented in Chapter 7. RMS measurement errors of 0.11-0.13 m were obtained in some tests, but a sensitivity to camera alignment was discovered in others which negatively affected results. A very stiff stereo camera mount with a sub-0.5 m baseline is suggested for future experiments. A summary of the main conclusions, a review of the objectives, and recommendations for future work are given in Chapter 8. Recommendations include further refinement of both sensors, an investigation into lighting sensitivity, and alternative applications of the sensors.This work was supported by a "CSIR South Africa Cambridge Scholarship", funded jointly by the Cambridge Commonwealth, European & International Trust and the Council for Scientific & Industrial Research (CSIR South Africa)