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Application of a Distributed Systems Architecture for Increased Speed in Image Processing on an Autonomous Ground Vehicle

机译:分布式系统架构在自动地面车辆图像处理中的应用

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This paper presents the application of a distributed systems architecture to an autonomous ground vehicle, Q, that participates in both the autonomous and navigation challenges of the Intelligent Ground Vehicle Competition. In the autonomous challenge the vehicle is required to follow a course, while avoiding obstacles and staying within the course boundaries, which are marked by white lines. For the navigation challenge, the vehicle is required to reach a set of target destinations, known as way points, with given GPS coordinates and avoid obstacles that it encounters in the process. Previously the vehicle utilized a single laptop to execute all processing activities including image processing, sensor interfacing and data processing, path planning and navigation algorithms and motor control. National Instruments' (NI) Lab VIEW served as the programming language for software implementation. As an upgrade to last year's design, a NI compact Reconfigurable Input/Output system (cRIO) was incorporated to the system architecture. The cRIO is NI's solution for rapid prototyping that is equipped with a real time processor, an FPGA and modular input/output. Under the current system, the real time processor handles the path planning and navigation algorithms, the FPGA gathers and processes sensor data. This setup leaves the laptop to focus on running the image processing algorithm. Image processing as previously presented by Nepal et. al. is a multi-step line extraction algorithm and constitutes the largest processor load. This distributed approach results in a faster image processing algorithm which was previously Q's bottleneck. Additionally, the path planning and navigation algorithms are executed more reliably on the real time processor due to the deterministic nature of operation. The implementation of this architecture required exploration of various inter-system communication techniques. Data transfer between the laptop and the real time processor using UDP packets was established as the most reliable protocol after testing various options. Improvement can be made to the system by migrating more algorithms to the hardware based FPGA to further speed up the operations of the vehicle.
机译:本文介绍了分布式系统体系结构在自动地面车辆Q上的应用,该系统同时参加了智能地面车辆竞赛的自动驾驶和导航挑战。在自主挑战中,要求车辆遵循路线,同时避免障碍物并停留在以白线标记的路线边界内。对于导航挑战,要求车辆以给定的GPS坐标到达一组目标目的地(称为路标),并避免其在过程中遇到的障碍。以前,车辆使用单个笔记本电脑来执行所有处理活动,包括图像处理,传感器接口和数据处理,路径规划和导航算法以及电机控制。美国国家仪器(NI)的Lab VIEW是实现软件的编程语言。作为对去年设计的升级,NI紧凑型可重配置输入/输出系统(cRIO)已集成到系统架构中。 cRIO是NI的快速原型解决方案,配备了实时处理器,FPGA和模块化输入/输出。在当前系统下,实时处理器处理路径规划和导航算法,FPGA收集并处理传感器数据。此设置使笔记本电脑可以专注于运行图像处理算法。尼泊尔等人先前介绍的图像处理。等是一种多步线提取算法,构成最大的处理器负载。这种分布式方法导致了更快的图像处理算法,该算法以前是Q的瓶颈。另外,由于操作的确定性,路径规划和导航算法在实时处理器上更可靠地执行。这种体系结构的实现需要探索各种系统间通信技术。在测试了各种选项之后,使用UDP数据包的便携式计算机和实时处理器之间的数据传输被确立为最可靠的协议。通过将更多算法移植到基于硬件的FPGA,可以进一步提高车辆的运行速度,从而对系统进行改进。

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