Predictive Maneuver Planning and Control of an Autonomous Vehicle in Multi-Vehicle Traffic with Observation Uncertainty

Autonomous vehicle technology is a promising development for improving the safety, efficiency and environmental impact of on-road transportation systems. However, the task of guiding an autonomous vehicle by rapidly and systematically accommodating the plethora of changing constraints, e.g. of avoiding multiple stationary and moving obstacles, obeying traffic rules, signals and so on as well as the uncertain state observation due to sensor imperfections, remains a major challenge. This dissertation attempts to address this challenge via designing a robust and efficient predictive motion planning framework that can generate the appropriate vehicle maneuvers (selecting and tracking specific lanes, and related speed references) as well as the constituent motion trajectories while considering the differential vehicle kinematics of the controlled vehicle and other constraints of operating in public traffic. The main framework combines a finite state machine (FSM)-based maneuver decision module with a model predictive control (MPC)-based trajectory planner. Based on the prediction of the traffic environment, reference speeds are assigned to each lane in accordance with the detection of objects during measurement update. The lane selection decisions themselves are then incorporated within the MPC optimization. The on-line maneuver/motion planning effort for autonomous vehicles in public traffic is a non-convex problem due to the multiple collision avoidance constraints with overlapping areas, lane boundaries, and nonlinear vehicle-road dynamics constraints. This dissertation proposes and derives some remedies for these challenges within the planning framework to improve the feasibility and optimality of the solution. Specifically, it introduces vehicle grouping notions and derives conservative and smooth algebraic models to describe the overlapped space of several individual infeasible spaces and help prevent the optimization from falling into undesired local minima. Furthermore, in certain situations, a forced objective selection strategy is needed and adopted to help the optimization jump out of local minima. Furthermore, the dissertation considers stochastic uncertainties prevalent in dynamic and complex traffic and incorporate them with in the predictive planning and control framework. To this end, Bayesian filters are implemented to estimate the uncertainties in object motions and then propagate them into the prediction horizon. Then, a pair-wise probabilistic collision condition is defined for objects with non-negligible geometrical shape/sizes and computationally efficient and conservative forms are derived to efficiently and analytically approximate the involved multi-variate integrals. The probabilistic collision evaluation is then applied within a vehicle grouping algorithms to cluster the object vehicles with closeness in positions and speeds and eventually within the stochastic predictive maneuver planner framework to tighten the chanced-constraints given a deterministic confidence margin. It is argued that these steps make the planning problem tractable for real-time implementation on autonomously controlled vehicles

Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

An Original Approach for a Better Remote Control of an Assistive Robot

Many researches have been done in the field of assistive robotics in the last few years. The first application field was helping with the disabled people\\u27s assistance. Different works have been performed on robotic arms in three kinds of situations. In the first case, static arm, the arm was principally dedicated to office tasks like telephone, fax... Several autonomous modes exist which need to know the precise position of objects. In the second configuration, the arm is mounted on a wheelchair. It follows the person who can employ it in more use cases. But if the person must stay in her/his bed, the arm is no more useful. In a third configuration, the arm is mounted on a separate platform. This configuration allows the largest number of use cases but also poses more difficulties for piloting the robot. The second application field of assistive robotics deals with the assistance at home of people losing their autonomy, for example a person with cognitive impairment. In this case, the assistance deals with two main points: security and cognitive stimulation. In order to ensure the safety of the person at home, different kinds of sensors can be used to detect alarming situations (falls, low cardiac pulse rate...). For assisting a distant operator in alarm detection, the idea is to give him the possibility to have complementary information from a mobile robot about the person\\u27s activity at home and to be in contact with the person. Cognitive stimulation is one of the therapeutic means used to maintain as long as possible the maximum of the cognitive capacities of the person. In this case, the robot can be used to bring to the person cognitive stimulation exercises and stimulate the person to perform them. To perform these tasks, it is very difficult to have a totally autonomous robot. In the case of disabled people assistance, it is even not the will of the persons who want to act by themselves. The idea is to develop a semi-autonomous robot that a remote operator can manually pilot with some driving assistances. This is a realistic and somehow desired solution. To achieve that, several scientific problems have to be studied. The first one is human-machine-cooperation. How a remote human operator can control a robot to perform a desired task? One of the key points is to permit the user to understand clearly the way the robot works. Our original approach is to analyse this understanding through appropriation concept introduced by Piaget in 1936. As the robot must have capacities of perceptio

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

Towards autonomous robotic systems: seamless localization and trajectory planning in dynamic environments

Evolucionar hacia una sociedad más automatizada y robotizada en la que podamos convivir con sistemas robóticos que desempeñen tareas poco atractivas o peligrosas para el ser humano, supone plantearnos, entre otras cuestiones, qué soluciones existen actualmente y cuáles son las mejoras a incorporar a las mismas. La mayoría de aplicaciones ya desarrolladas son soluciones robustas y adecuadas para el fin que se diseñan. Sin embargo, muchas de las técnicas implantadas podrían funcionar de manera más eficiente o bien adaptarse a otras necesidades. Asimismo, en la mayoría de aplicaciones robóticas adquiere importancia el contexto en el que desempeñan su función. Hay entornos estructurados y fáciles de modelar, mientras que otros apenas presentan características utilizables para obtener información de los mismos.Esta tesis se centra en dos de las funciones básicas que debe tener cualquier sistema robótico autónomo para desplazarse de forma robusta en cualquier tipo de entorno: la localización y el cálculo de trayectorias seguras. Además, los escenarios en los que se desea poner en práctica la investigación son complejos: un parque industrial con zonas cuyas características de entorno (usualmente geométricas) son utilizadas para que un robot se localice, varían; y entornos altamente ocupados por otros agentes móviles, como el vestíbulo de un teatro, en los que se debe considerar las características dinámicas de los demás para calcular un movimiento que sea seguro tanto para el robot como para los demás agentes.La información que se puede percibir de los escenarios con ambientes no homogéneos, por ejemplo de interior y exterior, suele ser de características diferentes. Cuando la información que se dispone del entorno proviene de sensores diferentes hay que definir un método que integre las medidas para tener una estimación de la localización del robot en todo momento. El tema de la localización se ha investigado intensamente y existen soluciones robustas en interior y exterior, pero no tanto en zonas mixtas. En las zonas de transición interior-exterior y viceversa es necesario utilizar sensores que funcionan correctamente en ambas zonas, realizando una integración sensorial durante la transición para evitar discontinuidades en la localización o incluso que el robot se pierda. De esta manera la navegación autónoma, dependiente de la correcta localización, funcionará sin discontinuidades ni movimientos bruscos.En entornos dinámicos es esencial definir una forma de representar la información que refleje su naturaleza cambiante. Por ello, se han definido en la literatura diferentes modelos que representan el dinamismo del entorno, y que permiten desarrollar una planificación de trayectorias directamente sobre las variables que controlan el movimiento del robot, en nuestro caso, las velocidades angular y lineal para un robot diferencial. Los planificadores de trayectorias y navegadores diseñados para entornos estáticos no funcionan correctamente en escenarios dinámicos, ya que son puramente reactivos. Es necesario tener en cuenta la predicción del movimiento de los obstáculos móviles para planificar trayectorias seguras sin colisión. Los temas abordados y las contribuciones aportadas en esta tesis son:• Diseño de un sistema de localización continua en entornos de interior y exterior, poniendo especial interés en la fusión de las medidas obtenidas de diferentes sensores durante las transiciones interior-exterior, aspecto poco abordado en la literatura. De esta manera se obtiene una estimación acotada de la localización durante toda la navegación del robot. Además, la localización se integra con una técnica reactiva de navegación, construyendo un sistema completo de navegación. El sistema integrado se ha evaluado en un escenario real de un parque industrial, para una aplicación logística en la que las transiciones interior-exterior y viceversa suponían un problema fundamental a resolver.• Definición de un modelo para representar el entorno dinámico del robot, llamado Dynamic Obstacle Velocity-Time Space (DOVTS). En este modelo aparecen representadas las velocidades permitidas y prohibidas para que el robot evite las colisiones con los obstáculos de alrededor. Este modelo puede ser utilizado por algoritmos de navegación ya existentes, y sirve de base para las nuevas técnicas de navegación desarrolladas en la tesis y explicadas en los siguientes puntos. • Desarrollo de una técnica de planificación y navegación basada en el modelo DOVTS. En este modelo se identifica un conjunto de situaciones relativas entre el robot y los obstáculos. A cada situación se asocia una estrategia de navegación, que considera la seguridad del robot para evitar colisiones, a la vez que intenta minimizar el tiempo al objetivo.• Implementación de una técnica de planificación y navegación basada en el modelo DOVTS, que utiliza explícitamente la información del tiempo para la planificación del movimiento. Se desarrolla un algoritmo A*-like que planifica los movimientos de los siguientes instantes, incrementando la maniobrabilidad del robot para la evitación de obstáculos respecto al método del anterior punto, a costa de un mayor tiempo de cómputo. Se analizan las diferencias en el comportamiento global del robot con respecto a la técnica anterior.Los diferentes aspectos que se han investigado en esta tesis tratan de avanzar en el objetivo de conseguir robots autónomos que puedan adaptarse a nuestra vida cotidiana en escenarios que son típicamente dinámicos de una forma natural y segura.<br /

Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects

Inevitably, autonomous vehicles need to interact with other road participants in a variety of highly complex or critical driving scenarios. It is still an extremely challenging task even for the forefront companies or institutes to enable autonomous vehicles to interactively predict the behavior of others, and plan safe and high-quality motions accordingly. The major obstacles are not just originated from prediction and planning algorithms with insufficient performances. Several fundamental problems in the fields of interactive prediction and planning still remain open, such as formulation, representation and evaluation of interactive prediction methods, motion dataset with densely interactive driving behavior, as well as interface of interactive prediction and planning algorithms. The aforementioned fundamental aspects of interactive prediction and planning are addressed in this dissertation along with various kinds of algorithms. First, generic environmental representation for various scenarios with topological decomposition is constructed, and a corresponding planning algorithm is designed by combining graph search and optimization. Hard constraints in optimization-based planners are also incorporated into the training loss of imitation learning so that the policy net can generate safe and feasible motions in highly constrained scenarios. Unified problem formulation and motion representation are designed for different paradigms of interactive predictors such as planning-based prediction (inverse reinforcement learning), as well as probabilistic graphical models (hidden Markov model) and deep neural networks (mixture density network), which are utilized for the prediction/planning interface design and prediction benchmark. A framework combing decision network and graph-search/optimization/sample-based planner is proposed to achieve a driving strategy which is defensive to potential violations of others, but not overly conservatively to threats of low probabilities. Such driving strategy is achieved via experiments based on the aforementioned interactive prediction and planning algorithms with proper interface designed. These predictors are also evaluated from closed loop perspective considering planning fatality when using the prediction results instead of pure data approximation metrics. Finally, INTERACTION (INTERnational, Adversarial and Cooperative moTION) dataset with highly interactive driving scenarios and behavior from international locations is constructed with interaction density metric defined to compare different datasets. The dataset has been utilized for various behavior-related research areas such as prediction, planning, imitation learning and behavior modeling, and is inspiring new research fields such as representation learning, interaction extraction and scenario generation

Computing fast search heuristics for physics-based mobile robot motion planning

Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety. Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles. Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner. The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place. The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains. The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution. With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself. The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints

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