Path following hybrid control for vehicle stability applied to industrial forklifts

The paper focuses on a closed-loop hybrid controller (kinematic and dynamic) for path following approaches with industrial forklifts carrying heavy loads at high speeds, where aspects such as vehicle stability, safety, slippage and comfort are considered. The paper first describes a method for generating Double Continuous Curvature (DCC) paths for non-holonomic wheeled mobile robots, which is the basis of the proposed kinematic controller. The kinematic controller generates a speed profile, based on slow-in and fast-out policy, and a curvature profile recomputing DCC paths in closed-loop. The dynamic controller determines maximum values for decelerations and curvatures, as well as bounded sharpness so that instantaneous vehicle stability conditions can be guaranteed against lateral and frontal tip-overs. One of the advantages of the proposed method, with respect to full dynamic controllers, is that it does not require dynamic parameters to be estimated for modelling, which in general can be a difficult task. The proposed kinematic dynamic controller is afterwards compared with a classic kinematic controller like Pure-Pursuit. For that purpose, in our hybrid control structure we have just replaced the proposed kinematic controller with Pure-Pursuit. Several metrics, such as settling time, overshoot, safety and comfort have been analysed.This work was supported by VALi+d and PROMETEO Programs (Conselleria d'Educacio, Generalitat Valenciana), DIVISAMOS (DPI-2009-14744-C03-01) and SAFEBUS (IPT-2011-1165-370000): Ministry of Economy and Competitivity.Girbés, V.; Armesto Ángel, L.; Tornero Montserrat, J. (2014). Path following hybrid control for vehicle stability applied to industrial forklifts. Robotics and Autonomous Systems. 62(6):910-922. https://doi.org/10.1016/j.robot.2014.01.004S91092262

Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

[EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072TESI

Hazard avoidance for high-speed rough-terrain unmanned ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005."June 2005."Includes bibliographical references (p. 111-116).High-speed unmanned ground vehicles have important applications in rough-terrain. In these applications unexpected and dangerous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform navigation and hazard avoidance calculations based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot's performance due to model parameter and sensor uncertainty. This thesis presents the development and analysis of a novel method for high speed navigation and hazard avoidance. The method is based on the two dimensional "trajectory space," which is a compact model-based representation of a robot's dynamic performance limits on natural terrain. This method allows a vehicle to perform dynamically feasible hazard avoidance maneuvers in a computationally efficient manner. This thesis also presents a novel method for trajectory replanning, based on a "curvature matching" technique. This method quickly generates a path connects the end of the path generated by a hazard avoidance maneuver to the nominal desired path. Simulation and experimental results with a small gasoline-powered high-speed unmanned ground vehicle verify the effectiveness of these algorithms. The experimental results demonstrate the ability of the algorithm to account for multiple hazards, varying terrain inclination, and terrain roughness. The experimental vehicle attained speeds of 8 m/s (18 mph) on flat and sloped terrain and 7 m/s (16 mph) on rough terrain.by Matthew J. Spenko.Ph.D

An optimization-based formalism for shared autonomy in dynamic environments

Teleoperation is an integral component of various industrial processes. For example, concrete spraying, assisted welding, plastering, inspection, and maintenance. Often these systems implement direct control that maps interface signals onto robot motions. Successful completion of tasks typically requires high levels of manual dexterity and cognitive load. In addition, the operator is often present nearby dangerous machinery. Consequently, safety is of critical importance and training is expensive and prolonged -- in some cases taking several months or even years. An autonomous robot replacement would be an ideal solution since the human could be removed from danger and training costs significantly reduced. However, this is currently not possible due to the complexity and unpredictability of the environments, and the levels of situational and contextual awareness required to successfully complete these tasks. In this thesis, the limitations of direct control are addressed by developing methods for shared autonomy. A shared autonomous approach combines human input with autonomy to generate optimal robot motions. The approach taken in this thesis is to formulate shared autonomy within an optimization framework that finds optimized states and controls by minimizing a cost function, modeling task objectives, given a set of (changing) physical and operational constraints. Online shared autonomy requires the human to be continuously interacting with the system via an interface (akin to direct control). The key challenges addressed in this thesis are: 1) ensuring computational feasibility (such a method should be able to find solutions fast enough to achieve a sampling frequency bound below by 40Hz), 2) being reactive to changes in the environment and operator intention, 3) knowing how to appropriately blend operator input and autonomy, and 4) allowing the operator to supply input in an intuitive manner that is conducive to high task performance. Various operator interfaces are investigated with regards to the control space, called a mode of teleoperation. Extensive evaluations were carried out to determine for which modes are most intuitive and lead to highest performance in target acquisition tasks (e.g. spraying/welding/etc). Our performance metrics quantified task difficulty based on Fitts' law, as well as a measure of how well constraints affecting the task performance were met. The experimental evaluations indicate that higher performance is achieved when humans submit commands in low-dimensional task spaces as opposed to joint space manipulations. In addition, our multivariate analysis indicated that those with regular exposure to computer games achieved higher performance. Shared autonomy aims to relieve human operators of the burden of precise motor control, tracking, and localization. An optimization-based representation for shared autonomy in dynamic environments was developed. Real-time tractability is ensured by modulating the human input with information of the changing environment within the same task space, instead of adding it to the optimization cost or constraints. The method was illustrated with two real world applications: grasping objects in cluttered environments and spraying tasks requiring sprayed linings with greater homogeneity. Maintaining motion patterns -- referred to as skills -- is often an integral part of teleoperation for various industrial processes (e.g. spraying, welding, plastering). We develop a novel model-based shared autonomous framework for incorporating the notion of skill assistance to aid operators to sustain these motion patterns whilst adhering to environment constraints. In order to achieve computational feasibility, we introduce a novel parameterization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids. This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a model predictive control loop. Our hardware realization validates the effectiveness of our method to recognize a change of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles. In addition, extensions of the work to supervisory control are described. An exploratory study presents an approach that improves computational feasibility for complex tasks with minimal interactive effort on the part of the human. Adaptations are theorized which might allow such a method to be applicable and beneficial to high degree of freedom systems. Finally, a system developed in our lab is described that implements sliding autonomy and shown to complete multi-objective tasks in complex environments with minimal interaction from the human

Generación de Trayectorias de Curvatura Continua para el Seguimiento de Líneas basado en Visión Artificial

Desarrollo matemático y análisis de nuevas técnicas para la generación de trayectorias de curvatura continua aplicado al problema del seguimiento de línea con curvatura y brusquedad acotadas.Girbés Juan, V. (2010). Generación de Trayectorias de Curvatura Continua para el Seguimiento de Líneas basado en Visión Artificial. http://hdl.handle.net/10251/12881Archivo delegad

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered