Long-range navigation in complex and dynamic environments with Full-Stack S-DOVS

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios

DISEÑO Y CONSTRUCCIÓN DE UN ROBOT PARA DESINFECCIÓN DE SUPERFICIES CON LUZ ULTRAVIOLET

En respuesta a la emergencia sanitaria producto del COVID-19 se diseña un prototipo de robot móvil con el objetivo de automatizar los protocolos de desinfección en los salones de la universidad industrial de Santander en preparación para el eventual retorno a la presencialidad. Este prototipo hace uso de lámparas de luz ultravioleta tipo C ampliamente utilizadas para el tratamiento de agua y aire en espacios cerrados pero que debido al riesgo que representan para los seres humanos apenas comienza a implementarse en otros ambientes con ayuda de robots y sistemas de automatización. Se realizó entonces el diseño en detalle que involucra la parte mecánica, electrónica y de software basado en los requerimientos correspondientes. La fabricación del prototipo transcurre durante la emergencia sanitaria y por lo tanto se busca que en su mayoría pueda ser construido desde casa con poca ayuda de terceros y con componentes disponibles en el mercado local. Para la capa de software se hace uso de la plataforma ROS que facilita la programación de robots autónomos y se implementó una aplicación móvil para permitir el manejo remoto. Finalmente, se realizaron las pruebas de operación en los salones de la UIS y se concluyó que el sistema realizado puede cumplir su función de forma efectiva, pero hacen falta varias mejoras tanto en hardware como en software para optimizar su desempeño y confiabilidad

Real-time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments

Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performance-based trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions: * How should a trajectory planning algorithm be formulated to enable automated ground vehicle safety and performance in unknown dynamic environments? * How can such an algorithm be solved in real-time? Safe trajectory planning for high-performance automated vehicles with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time to goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This thesis presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a high mobility multipurpose wheeled vehicle (HMMWV), that includes the above set of specifications. This formulation is tested then with various sets of these specifications in a known dynamic environment. In particular, a parametric study relating execution horizon and obstacle speed reveals that the moving obstacle avoidance specification is not needed for safety when the planner has a short execution horizon (< 0.375 s), and the obstacles are slow (< 2.11 m/s). However, a moving obstacle avoidance specification is needed when the obstacles move faster, and this specification improves safety without, in most cases, increasing solve-times. Overall, results indicate that trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above unless a static or low-speed environment permits a less comprehensive planner. Then, this thesis combines this comprehensive planning algorithm with a suitable perception algorithm to enable safe and performant control of automated ground vehicles in unknown dynamic environments. A high-fidelity, ROS-based proving ground with a 2D LiDAR model, in Gazebo, and a 145 degree of freedom model of the HMMWV, in Chrono, is developed to combine these algorithms. Six-hundred tests, realized with various obstacle speeds and sizes, are performed in this proving ground in both known and unknown dynamic environments. Results from this comparison demonstrate that operating in an unknown environment, as opposed to a known environment, significantly increases collisions, steering effort, throttle effort, braking effort, orientation and tracking error, time to goal, and planner solve times. To avoid this deterioration of safety and performance factors in unknown environments, the use of more accurate perception systems should be explored. Ultimately, however, these results demonstrate that the comprehensive trajectory planning formulation developed in this thesis enables safe and performant control of automated vehicles in unknown dynamic environments among small (< 2 m) obstacles traveling at speeds up to high (20 m/s). To solve this formulation in real-time, an open-source, direct-collocation-based optimal control problem modeling language, called NLOptControl, is established in this thesis. Results demonstrate that NLOptControl can solve the formulation in real-time in both known and unknown environments. NLOptControl holds great potential for not only improving existing off-line and on-line control systems but also engendering a wide variety of new ones.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149859/1/febbo_1.pd