Integration of fault tolerance and hardware redundancy techniques into the design of mobile platforms

This work addresses the development of a fault-tolerant mobile platform. Fault-tolerant mechanical system design is an emerging technology that attempts to build highly reliable systems by incorporating hardware and software architectures. For this purpose, previous work in fault-tolerant were reviewed. Alternate architectures were evaluated to maximize the fault tolerance capabilities of the driving and steering systems of a mobile platform. The literature review showed that most of the research work on fault tolerance has been done in the area of kinematics and control systems of robotic arms. Therefore, hardware redundancy and fault tolerance in mobile robots is an area to be researched. The prototype constructed as part of this work demonstrated basic principles and uses of a fault-tolerant mechanism, and is believed to be the first such system in its class. It is recommended that different driving and steering architectures, and the fault-tolerant controllers\u27 performance be tested on this prototype

AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Model Predictive Control as a Function for Trajectory Control during High Dynamic Vehicle Maneuvers considering Actuator Constraints

Autonomous driving is a rapidly growing field and can bring significant transition in mobility and transportation. In order to cater a safe and reliable autonomous driving operation, all the systems concerning with perception, planning and control has to be highly efficient. MPC is a control technique used to control vehicle motion by controlling actuators based on vehicle model and its constraints. The uniqueness of MPC compared to other controllers is its ability to predict future states of the vehicle using the derived vehicle model. Due to the technological development & increase in computational capacity of processors and optimization algorithms MPC is adopted for real-time application in dynamic environments. This research focuses on using Model predictive Control (MPC) to control the trajectory of an autonomous vehicle controlling the vehicle actuators for high dynamic maneuvers. Vehicle Models considering kinematics and vehicle dynamics is developed. These models are used for MPC as prediction models and the performance of MPC is evaluated. MPC trajectory control is performed with the minimization of cost function and limiting constraints. MATLAB/Simulink is used for designing trajectory control system and interfaced with CarMaker for evaluating controller performance in a realistic simulation environment. Performance of MPC with kinematic and dynamic vehicle models for high dynamic maneuvers is evaluated with different speed profiles

Prioritized Obstacle Avoidance in Motion Planning of Autonomous Vehicles

Driver errors are a critical factor of the majority of car crashes. Autonomous vehicles take drivers and driver errors out of the equation, so they are being developed to reduce car crashes. However, in some situations, a crash is unavoidable even for an autonomous vehicle. An autonomous vehicle is expected to behave properly in such a situation. Crashing into different obstacles have different costs based on the injury or damage the crash might cause. In an imminent crash situation, an autonomous vehicle is expected to consider these costs and plan a trajectory that avoids the obstacles with the highest priorities. In this thesis, a motion planning Model Predictive Controller (MPC) has been developed that plans the vehicle’s trajectories based on the obstacle’s priorities. Motion planning MPCs usually use potential fields or obstacle constraints for obstacle avoidance. However, they treat all the obstacles in the same way. Two methods have been developed in this thesis to prioritize obstacles in motion planning. The first method prioritizes obstacles based on their avoidance necessities. It categorizes obstacles as crossable and non-crossable, and assigns a potential function to each category corresponding to its avoidance necessity. The second method prioritizes obstacles based on their corresponding crash costs. It applies lexicographic optimization on the MPC to prioritize the non-crossable obstacles according to their crash costs by prioritizing their corresponding constraints. A motion planning MPC problem is generally a nonlinear MPC problem. It is usually approximated by a quadratic MPC problem to become implementable in real time. In this thesis, a quadratic motion planning MPC has been developed. This MPC has a linear vehicle model and linear vehicle and obstacle constraints. The linear vehicle model along with the linear vehicle constraints should be able to model the nonlinear vehicle behavior. A linear bicycle model has been utilized, and linear tire constraints have been developed such that they can model the nonlinear vehicle behavior at the tire force limits. Moreover, a linear obstacle constraint set misses some of the feasible trajectories in the process of convexifying the obstacle-free area. An iterative obstacle avoidance method has been developed in this thesis to reduce the number of feasible trajectories missed due to the convexification. The performance of the developed motion planning MPC has been evaluated in a computer simulation with a high fidelity vehicle model. The MPC has been simulated for test scenarios to evaluate its performance in autonomous driving and prioritizing obstacles. The capabilities of the developed tire constraints and the iterative obstacle avoidance method have also been observed. The motion planning MPC has also been implemented on an autonomous test vehicle platform to show that it is implementable in real time and to validate the simulation results

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Authority-Sharing Control of Assistive Robotic Walkers

A recognized consequence of population aging is a reduced level of mobility, which undermines the life quality of several senior citizens. A promising solution is represented by assisitive robotic walkers, combining the benefits of standard walkers (improved stability and physical support) with sensing and computing ability to guarantee cognitive support. In this context, classical robot control strategies designed for fully autonomous systems (such as fully autonomous vehicles, where the user is excluded from the loop) are clearly not suitable, since the user’s residual abilities must be exploited and practiced. Conversely, to guarantee safety even in the presence of user’s cognitive deficits, the responsibility of controlling the vehicle motion cannot be entirely left to the assisted person. The authority-sharing paradigm, where the control authority, i.e., the capability of controlling the vehicle motion, is shared between the human user and the control system, is a promising solution to this problem. This research develops control strategies for assistive robotic walkers based on authority-sharing: this way, we ensure that the walker provides the user only the help he/she needs for safe navigation. For instance, if the user requires just physical support to reach the restrooms, the robot acts as a standard rollator; however, if the user’s cognitive abilities are limited (e.g., the user does not remember where the restrooms are, or he/she does not recognize obstacles on the path), the robot also drives the user towards the proper corridors, by planning and following a safe path to the restrooms. The authority is allocated on the basis of an error metric, quantifying the distance between the current vehicle heading and the desired movement direction to perform the task. If the user is safely performing the task, he/she is endowed with control authority, so that his/her residual abilities are exploited. Conversely, if the user is not capable of safely solving the task (for instance, he/is going to collide with an obstacle), the robot intervenes by partially or totally taking the control authority to help the user and ensure his/her safety (for instance, avoiding the collision). We provide detailed control design and theoretical and simulative analyses of the proposed strategies. Moreover, extensive experimental validation shows that authority-sharing is a successful approach to guide a senior citizen, providing both comfort and safety. The most promising solutions include the use of haptic systems to suggest the user a proper behavior, and the modification of the perceived physical interaction of the user with the robot to gradually share the control authority using a variable stiffness vehicle handling

Shared control strategies for automated vehicles

188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada

Development of a Model-based Control Strategy for Autonomous Vehicle Collision Avoidance

Human inattention is the leading cause of traffic accidents in many regions around the world. Autonomous vehicle technologies are rapidly emerging with the aim to remove the human factor in key driving procedures, such as perception, decision-making, path planning, and control. These technologies are subject to technological, ethical, and social scrutiny; therefore, extensive work is required to instill confidence in the reliability of these automated driving features. One key responsibility of automated driving is in planning and tracking a trajectory to avoid collisions with obstacles, such as other vehicles. One of the foremost challenges in the formulation of a feasible path is considering the dynamics and constraints of the vehicle and the environment. Model predictive control (MPC) is one of the most common control techniques for its ability to handle constraints. For this reason, MPC has been widely studied for path planning and tracking for autonomous vehicles and mobile robots. MPC relies upon an accurate vehicle dynamics model which enables accurate state predictions, thereby resulting in effective control actions to achieve the desired objective. It is challenging, however, to capture all of the details and uncertainties of the dynamics associated with a vehicle. In particular, modeling tire dynamics requires detailed nonlinear models to fully reflect the vehicle behavior. One common technique for motion planning using MPC is to employ artificial potential fields (PFs) which generate an artificial repulsive force from obstacles or road boundaries to influence the controller to track the vehicle along a safe trajectory. Some state-of-the-art PF-based techniques include the PF intensity directly in the MPC objective function, thereby considering the vehicle constraints and dynamics as part of the path planning. In this thesis, an enhanced PF-based motion controller is presented. The control design uses MPC with a detailed dynamics model; the model considers the combined-slip effect on tire forces, nonlinearities, and actuator dynamics. Therefore, it offers an improvement upon prior studies which rely upon simplified dynamics models. Moreover, the PF intensity is included in the objective function, like prior studies, although the PF approximation is further simplified by only considering the lateral component of the repulsive force as part of the latera controller. A separate, novel longitudinal control policy uses the longitudinal component of the PF gradient to regulate the speed setpoint when approaching an obstacle in the same lane; subsequently, proportional-integral-derivative (PID) controllers command axle torque and brake pressure to track the reference speed. The developed controller and dynamics model are validated in both simulation and physical vehicle tests. To emulate the various driving scenarios where avoidance or stopping is required, a virtual driving environment is employed: simulated obstacles are placed in the roadway, the detections of which are sent to the controller. The controller performance is demonstrated in various evasive maneuvers, and in different road conditions