Distributed Optimization of Field Sensors Guided Autonomous Vehicular Operations

现场传感器引导自主车辆操作的分布式优化

• 批准号: RGPIN-2019-06368
• 负责人: Chen, Xiang
• 金额: $ 3.35万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738869
• 关键词: Distributed; Optimization; Field; Sensors; Guided

Autonomous robotic vehicles, ground or aerial, play critical roles in various intelligent operations seen or desired in our current and future lives, ranging from home services and industrial manufacturing, to more and more field applications such as natural resource monitoring, pipeline and building inspection, precision agriculture, mineral exploration, search and rescue operations, etc. Due to the increasing complexity in functioning expectation, there is no doubt that networked and collaborative operation of autonomous robotic vehicles will be in high demand. It is well known that one of the biggest challenges facing autonomous vehicles is the real-time sensing and perception of their surrounding environment. The current sensing technologies facilitate the broad application of various field sensors such as cameras, LIDAR/RADAR, ultrasonic sensors, etc. However, due to the nature of field perception and measurements performed by these sensing devices, which essentially is an infinite-dimension problem in space, huge challenges are posed for effective and efficient applications of them, especially, when applied in autonomous operations which normally demand tremendous robustness of performance. Another major concern in practices about field sensing devices is the cost of devices themselves, whereas the realities in autonomous operations call for solutions of networked low-cost sensing units instead of powerful single or multiple high-cost units. Apparently, it would be of great significance for both research and industrial applications to dig out the greatest potential of field sensing and perception as applied in various autonomous operations (for example, autonomous operation of ground or aerial robotic vehicles) so that these operations could be delivered in a way that is both the most effective and the most cost efficient. This research proposal intends to address the distributed optimization for field sensing networks with the operation of autonomous robotic vehicles being the showcase of application. It is noted that the focus will be on developing a systematic design framework which will be featured with novel modeling thinking, new concepts of problem formulation, practically feasible design procedures and algorithms, and meaningful applications to autonomous robotic operation tasks. In particular, it is expected that the sensing functions and robotic vehicle operations are integrated to generate conceptually new methods to address the said problems and to design robust and optimal control and management mechanisms for the networked autonomous robotic vehicles. It is also argued that the framework developed would be highly industrial-practices oriented and hence would find its way to be easily applied in practices.

自主机器人车，地面或空中，在我们当前和未来的生活中看到或期待的各种智能操作中起着至关重要的作用车辆的需求量很高。众所周知，自动驾驶汽车面临的最大挑战之一是对周围环境的实时感知和感知。当前的传感技术有助于广泛应用各种现场传感器，例如摄像机，雷达/雷达，超声传感器等。然而，由于这些传感设备所执行的野外感知和测量的性质，这本质上是空间中的无限尺度问题，尤其是在空间中构成巨大的挑战，尤其是在应用程序中的高效应用，该绩效是在运作中的有效应用，该绩效是自动的。关于现场传感设备的实践的另一个主要问题是设备本身的成本，而自动操作中的现实要求建立网络低成本感应单元的解决方案，而不是强大的单个或多个高成本单元。显然，对于研究和工业应用来说，要挖掘出在各种自主操作中应用的最大的现场感应和感知潜力（例如，地面或空中机器人车辆的自主操作），以便这些操作可以以最有效和最具成本效益的方式传递这些操作，这将具有重要意义。该研究建议旨在解决针对现场传感网络的分布式优化，而自动驾驶机器人车的运行是应用程序的展示。据指出，重点将放在开发系统的设计框架上，该框架将以新颖的建模思维，问题制定的新概念，实际上可行的设计程序和算法以及对自动机器人操作任务的有意义的应用。特别是，预计感应功能和机器人车辆操作已集成以生成概念上的新方法，以解决上述问题并为网络自动驾驶机器人车设计强大，最佳的控制和管理机制。还认为，开发的框架将是高度以工业实践为导向的，因此可以找到容易应用的方式。