Synthesizing Executable Simulations from Structural Models of Component-Based Systems

Experts in robotics systems have developed substantial software tools for simulation, execution, and hardware-in-the-loop testing. Unfortunately, many of these robotics-domain software infrastructures pose challenges for a robotics expert to use, unless that robotics expert is also familiar with middleware programming, and the integration of heterogeneous simulation tools. In this paper, we describe a novel modeling language designed to bridge these two domains in an intuitive visual representation. Using this metamodel-defined modeling language, we can design and build structural models of robotics systems, and synthesize experiments from these constructed models. The restrictions implicit (and explicit) in the visual language guide modelers to build only models that can be synthesized, a "correct by construction" approach. We discuss the impact of this language with a running example of an autonomous ground vehicle, and the hundreds of configuration parameters and several simulation tools that are necessary in order to simulate this complex example

The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.Comment: In Proceedings SCAV 2018, arXiv:1804.0340

The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.National Science Foundation; Air Force Office of Scientific Research [1253334, 1262960, 1419419, 1446435, 1446690, 1446702, 1446715 1521617]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

On the Partitioning of Syntax and Semantics For Hybrid Systems Tools

Interchange formats are notoriously difficult to finish. That is, once one is developed, it is highly nontrivial to prove (or disprove) generality, and difficult at best to gain acceptance from all major players in the application domain. This paper addresses such a problem for hybrid systems, but not from the perspective of a tool interchange format, but rather that of tool availability in a toolbox. Through the paper we explain why we think this is a good approach for hybrid systems, and we also analyze the domain of hybrid systems to discern the semantic partitions that can be formed to yield a classification of tools based on their semantics. These discoveries give us the foundation upon which to build semantic capabilities, and to guarantee operational interaction between tools based on matched operational semantics

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

Autonomous vehicles: From vehicular control to traffic control

International audienc

Enabling Mixed Autonomy Traffic Control

We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable and affordable platform for experimental control. This paper introduces an extensible open-source hardware and software platform, enabling a team of 100 vehicles to execute several different vehicular control algorithms as a collaborative fleet, composed of three different makes and models, which drove 22752 miles in a combined 1022 hours, over 5 days in Nashville, TN in November 2022