Simulating use cases for the UAH autonomous electric car

2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for the UAH Autonomous Electric Car, related with typical driving scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the decision making framework of an autonomous electric vehicle. First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating System (ROS) framework that allows the fusion of multiple sensors and the real-time processing and communication of multiple processes in different embedded processors. Then, the paper focuses on the study of some of the most interesting driving scenarios such as: stop, pedestrian crossing, Adaptive Cruise Control (ACC) and overtaking, illustrating both the executive module that carries out each behaviour based on Petri nets and the trajectory and linear velocity that allows to quantify the accuracy and robustness of the architecture proposal for environment perception, navigation and planning on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri

A Multi-Vehicle Cooperative Localization Approach for an Autonomy Framework

Offensive techniques produced by technological advancement present opportunities for adversaries to threaten the operational advantages of our joint and allied forces. Combating these new methodologies requires continuous and rapid development towards our own set of \game-changing technologies. Through focused development of unmanned systems and autonomy, the Air Force can strive to maintain its technological superiority. Furthermore, creating a robust framework capable of testing and evaluating the principles that define autonomy allows for the exploration of future capabilities. This research presents development towards a hybrid reactive/deliberative architecture that will allow for the testing of the principles of task, cognitive, and peer flexibility. Specifically, this work explores peer flexibility in multi-robot systems to solve a localization problem using the Hybrid Architecture for Multiple Robots (HAMR) as a basis for the framework. To achieve this task a combination of vehicle perception and navigation tools formulate inferences on an operating environment. These inferences are then used for the construction of Factor Graphs upon which the core algorithm for localization implements iSAM2, a high performing incremental matrix factorization method. A key component for individual vehicle control within the framework is the Unified Behavior Framework (UBF), a behavior-based control architecture which uses modular arbitration techniques to generate actions that enable actuator control. Additionally, compartmentalization of a World Model is explored through the use of containers to minimize communication overhead and streamline state information. The design for this platform takes on a polymorphic approach for modularity and robustness enabling future development