MRI: Development of an Autonomous Robotic Vehicle Instrument (ARVIN)

该项目开发了一个研究和培训平台，作为评估机器人子系统和超级系统性能的工具，通过提供一个基线工具来衡量和评估给定自动驾驶汽车设计的各个部分的性能和能力，从而加速和加强自动驾驶汽车子系统和部件的演变。这种自主机器人车辆仪表(ARVIN)旨在允许多个仪器、执行器和软件/硬件子系统同时并行运行。由于这些项目运行的共同环境，ARVIN提供了有关项目的比较性能和能力的精确、稳健的衡量标准。ARVIN通过其以网络为中心的架构设计，本质上可以快速重新配置，使评估平台上的传感器和执行器能够快速、轻松地进行替换和增强；ARWIN并不是一组相同的机器人结构，它们可以连接和改造以移动或攀登。ARWIN测试、模拟和验证三个紧密耦合的层次化领域中的子系统和组件，这些领域被认为对机器人车辆的进步至关重要：-导航、导航和控制(GNC)，-传感器和执行器套件，以及-软件和网络体系结构。自主机器人车辆仪器由经过大量改装的Argo Conquest 6x6越野/两栖车辆和车辆的硬件在环(HIL)模拟器组成。这辆车将配备用于避免碰撞的超声波声纳和病态激光雷达，用于制导的光纤陀螺、IMU和NavCom Starfire差分GPS接收器，用于控制的奔腾级PC-104堆栈，用于视觉捕获的Videre MEGA-STC-VAR立体高速彩色相机。通常，由于建造任何类型的自动驾驶车辆的成本(在时间和金钱上)令人难以置信地高，除非原始车辆设计无法完成任务，否则设计很少修改。最初的权衡研究很少会被重新审视和探索，以确定本可以有什么不同的做法。因此，Arvin应该会对大多数自动驾驶汽车设计项目迄今缺失的那种权衡研究产生深远影响。更广泛的影响：阿尔文允许学生和研究人员在实际的、物理的、自主的漫游车上获得经验。这种在系统集成、传感器融合、实时系统的软件架构和实际控制系统实现方面的实践培训有很大的帮助。海军研究生院和NASA-Ames的智能机器人小组(IRG)已经表示有兴趣和热情合作和试验ARVIN将产生的模块化底盘。这些平台还将用于推广，有助于进一步了解移动机器人技术的权衡取舍。Arvin的基础设施应该成为具有成本效益的通用机器人费用，可以很容易地在其他机构复制。

This project, developing a platform for research and training to serve as an instrument for evaluating robotic subsystem and supersystem performance, accelerates and enhances the evolution of autonomous vehicle subsystems and component parts by providing a baseline instrument to measure and assess the performance and capabilities of the various portions of a given autonomous vehicle design. This Autonomous Robotic Vehicle Instrument (ARVIN) is designed to allow simultaneous, parallel operation of multiple instruments, actuators, and software/hardware subsystems. Due to the common environment in which the items are operating, the ARVIN yields a precise, robust metric of comparative performance and capability of the item in question. Inherently rapidly reconfigurable by design of its network-centric architecture, the ARVIN enables quick, easy substitutions and augmentations of sensors and actuators on the evaluation platform; it is not a set of identical robot structures that join and reform to move or climb.The ARWIN tests, simulates, and validates subsystems and components in three tightly coupled hierarchical areas acknowledge as critical to advances in robotics vehicles:-Guidance, Navigation, and Control (GNC),-Sensor and Actuators Suites, and-Software and Network Architectures.The Autonomous Robotic Vehicle Instrument consists of an Argo Conquest 6x6 Off-road/Amphibious vehicle, heavily modified, and a Hardware-in-the-Loop (HIL) simulator for the vehicle. The vehicle will be fitted with ultrasonic sonar and SICK LiDAR for collision avoidance, fiber optic gyros, IMU's, and NavCom STARFIRE differential GPS receivers for guidance, a Pentium class PC-104 stack with high speed communication networks for control, and a Videre MEGA-STC-VAR stereoscopic high speed color camera for vision capture.In general, due to the incredibly high cost (in time and money) of constructing an autonomous vehicle of any kind, designs are rarely modified unless the mission cannot be completed by the original vehicle design. Very rarely are the initial trade-off studies revisited and explored to see what might have been done differently. Thus, the ARVIN should have profound impact on the kind of trade-off studies that most autonomous vehicle design programs have to-date been missing. Broader Impact: ARVIN allows students and researchers to gain experience on an actual, physical, autonomous rover. This kind of practical training in systems integration, sensor fusion, software architecture for real-time systems, and actual control systems implementations has much to offer. The Naval Postgraduate School and the Intelligent Robotics Group (IRG) at NASA-Ames has indicated interest and enthusiasm in collaborating and experimenting with the modular chassis that will result of the ARVIN. Contributing to further understand trade-offs for mobile robotics, the platforms will also be used for outreach. The ARVIN infrastructure should become a cost-effective general robotics toll that may be easily replicated at other institutions.