Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles

A novel description of dual-envelop-oriented path tracking issue is presented for fully automated vehicles which considers shape of vehicle as inner-envelop (I-ENV) and feasible road region as outer-envelop (O-ENV). Then implicit linear model predictive control (MPC) approach is proposed to design moving horizon path tracking controller in order to solve the situations that may cause collision and run out of road in traditional path tracking method. The proposed MPC controller employed varied sample time and varied prediction horizon and could deal with modelling error effectively. In order to specify the effectiveness of the proposed dual-envelop-oriented moving horizon path tracking method, veDYNA-Simulink joint simulations in different running conditions are carried out. The results illustrate that the proposed path tracking scheme performs well in tracking the desired path, and could increase path tracking precision effectively

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Research studio for testing control algorithms of mobile robots

In recent years, a significant development of technologies related to the control and communication of mobile robots, including Unmanned Aerial Vehicles, has been noticeable. Developing these technologies requires having the necessary hardware and software to enable prototyping and simulation of control algorithms in laboratory conditions.The article presents the Laboratory of Intelligent Mobile Robots equipped with the latest solutions. The laboratory equipment consists of four quadcopter drones (QDrone) and two wheeled robots (QBot), equipped with rich sensor sets, a ground control station with Matlab-Simulink software, OptiTRACK object tracking system, and the necessary infrastructure for communication and security.The paper presents the results of measurements from sensors of robots monitoring various quantities during work. The measurements concerned, among others, the quantities of robots registered by IMU sensors of the tested robots (i.e., accelerometers, magnetometers, gyroscopes and others)

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations

Optimal Multi-UAV Trajectory Planning for Filming Applications

Teams of multiple Unmanned Aerial Vehicles (UAVs) can be used to record large-scale outdoor scenarios and complementary views of several action points as a promising system for cinematic video recording. Generating the trajectories of the UAVs plays a key role, as it should be ensured that they comply with requirements for system dynamics, smoothness, and safety. The rise of numerical methods for nonlinear optimization is finding a ourishing field in optimization-based approaches to multi- UAV trajectory planning. In particular, these methods are rather promising for video recording applications, as they enable multiple constraints and objectives to be formulated, such as trajectory smoothness, compliance with UAV and camera dynamics, avoidance of obstacles and inter-UAV con icts, and mutual UAV visibility. The main objective of this thesis is to plan online trajectories for multi-UAV teams in video applications, formulating novel optimization problems and solving them in real time. The thesis begins by presenting a framework for carrying out autonomous cinematography missions with a team of UAVs. This framework enables media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. Second, the thesis proposes a novel non-linear formulation for the challenging problem of computing optimal multi-UAV trajectories for cinematography, integrating UAV dynamics and collision avoidance constraints, together with cinematographic aspects such as smoothness, gimbal mechanical limits, and mutual camera visibility. Lastly, the thesis describes a method for autonomous aerial recording with distributed lighting by a team of UAVs. The multi-UAV trajectory optimization problem is decoupled into two steps in order to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages. This allows the trajectory planner to perform in real time and to react online to changes in dynamic environments. It is important to note that all the methods in the thesis have been validated by means of extensive simulations and field experiments. Moreover, all the software components have been developed as open source.Los equipos de vehículos aéreos no tripulados (UAV) son sistemas prometedores para grabar eventos cinematográficos, en escenarios exteriores de grandes dimensiones difíciles de cubrir o para tomar vistas complementarias de diferentes puntos de acción. La generación de trayectorias para este tipo de vehículos desempeña un papel fundamental, ya que debe garantizarse que se cumplan requisitos dinámicos, de suavidad y de seguridad. Los enfoques basados en la optimización para la planificación de trayectorias de múltiples UAVs se pueden ver beneficiados por el auge de los métodos numéricos para la resolución de problemas de optimización no lineales. En particular, estos métodos son bastante prometedores para las aplicaciones de grabación de vídeo, ya que permiten formular múltiples restricciones y objetivos, como la suavidad de la trayectoria, el cumplimiento de la dinámica del UAV y de la cámara, la evitación de obstáculos y de conflictos entre UAVs, y la visibilidad mutua. El objetivo principal de esta tesis es planificar trayectorias para equipos multi-UAV en aplicaciones de vídeo, formulando novedosos problemas de optimización y resolviéndolos en tiempo real. La tesis comienza presentando un marco de trabajo para la realización de misiones cinematográficas autónomas con un equipo de UAVs. Este marco permite a los directores de medios de comunicación diseñar misiones que incluyan diferentes tipos de tomas con una o varias cámaras, ejecutadas de forma secuencial o concurrente. En segundo lugar, la tesis propone una novedosa formulación no lineal para el difícil problema de calcular las trayectorias óptimas de los vehículos aéreos no tripulados en cinematografía, integrando en el problema la dinámica de los UAVs y las restricciones para evitar colisiones, junto con aspectos cinematográficos como la suavidad, los límites mecánicos del cardán y la visibilidad mutua de las cámaras. Por último, la tesis describe un método de grabación aérea autónoma con iluminación distribuida por un equipo de UAVs. El problema de optimización de trayectorias se desacopla en dos pasos para abordar los aspectos cinematográficos no lineales y la evitación de obstáculos en etapas separadas. Esto permite al planificador de trayectorias actuar en tiempo real y reaccionar en línea a los cambios en los entornos dinámicos. Es importante señalar que todos los métodos de la tesis han sido validados mediante extensas simulaciones y experimentos de campo. Además, todos los componentes del software se han desarrollado como código abierto

Research studio for testing control algorithms of mobile robots

In recent years, a significant development of technologies related to the control and communication of mobile robots, including Unmanned Aerial Vehicles, has been noticeable. Developing these technologies requires having the necessary hardware and software to enable prototyping and simulation of control algorithms in laboratory conditions.The article presents the Laboratory of Intelligent Mobile Robots equipped with the latest solutions. The laboratory equipment consists of four quadcopter drones (QDrone) and two wheeled robots (QBot), equipped with rich sensor sets, a ground control station with Matlab-Simulink software, OptiTRACK object tracking system, and the necessary infrastructure for communication and security.The paper presents the results of measurements from sensors of robots monitoring various quantities during work. The measurements concerned, among others, the quantities of robots registered by IMU sensors of the tested robots (i.e., accelerometers, magnetometers, gyroscopes and others)

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit

Design a Reliable Model Predictive Control for Path Following with Application to the Autonomous Vehicle and Considering Different Vehicle Models

Despite many advances that aim to bring autonomous cars to market, several challenges remain to overcome before such technology is widely adopted. Among these is the need for a reliable cornering control that can work efficiently in real-time. This thesis will focus on the successful development, implementation, and validation of reliable control algorithms for motion control of autonomous vehicles, which will give the University of Waterloo Watanomous team a competitive edge in the annual Auto-Drive competition. A reliable path following controller based on Model Predictive Control(MPC) and using different types of vehicle models are developed to 1) minimize the lateral distance between the vehicle and a reference path, 2) minimize the heading error of the vehicle, and 3) limit the rate of steering inputs to their saturation values and produce smooth motions. The control algorithm proposed in this thesis will rely on a known path as the desired input. Such input will be used to define a horizon over which reliable control actions can be computed to command the vehicle along the desired path with minimum lateral position error. The proposed MPC is implemented in MATLAB in which the associated Optimal Control Problem (OCP) is discretized using the direct Multiple Shooting method. MPC is tested using the CarSim vehicle simulator to validate its performance for double lane change(DLC) vehicle maneuver at constant low/high forward speeds and roads with different known friction coefficients. The effects of changing prediction horizon, sampling time, and weighting factors on path-following controllers’ performance are also discussed. Simulation results show that designed controllers pulled the system back to the predefined reference path, and tracking performances were satisfactory. It is also observed that MPC designed using a linear dynamic model and a combined single-track model, both had comparable performances and less lateral tracking error than MPC designed using the non-linear kinematic model for various speeds on the dry road, and at the speeds below 80kph on the wet road. Moreover, MPCs using linear dynamic and combined single-track vehicle models both worked in the shorter horizon. Overall, MPC using a combined single-track model showed enhanced performance by comparing the other two schemes for all the tests conducted on wet and dry roads at various vehicle speeds