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.

自主机器人车辆，地面或空中，在我们当前和未来生活中看到或期望的各种智能操作中发挥着关键作用，从家庭服务和工业制造，到越来越多的现场应用，如自然资源监测，管道和建筑检测，精准农业，矿产勘探，搜索和救援行动等。由于功能期望的日益复杂，毫无疑问，对自动驾驶机器人车辆的网络化和协同操作将有很高的需求。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。当前的传感技术促进了各种现场传感器的广泛应用，如相机、激光雷达/雷达、超声波传感器等。然而，由于这些传感设备执行的现场感知和测量的性质，本质上是空间中的无限维问题，因此对它们的有效和高效应用提出了巨大的挑战，特别是当应用于通常需要巨大性能鲁棒性的自主操作时。现场传感设备的另一个主要问题是设备本身的成本，而自主操作的现实需要网络化的低成本传感单元的解决方案，而不是强大的单个或多个高成本单元。显然，挖掘现场传感和感知在各种自主操作中的最大潜力（例如，地面或空中机器人车辆的自主操作）对于研究和工业应用都具有重要意义，以便这些操作可以以最有效和最具成本效益的方式交付。本研究计划旨在解决现场传感网络的分布式优化问题，并以自主机器人车辆的运行为应用展示。值得注意的是，重点将放在开发一个系统的设计框架上，该框架将具有新颖的建模思维，新的问题表述概念，实际可行的设计程序和算法，以及对自主机器人操作任务的有意义的应用。特别是，期望将传感功能和机器人车辆操作集成在一起，以产生概念上的新方法来解决上述问题，并为网络化自动机器人车辆设计鲁棒和最佳控制和管理机制。也有人认为，所开发的框架将高度面向工业实践，因此将找到易于在实践中应用的方法。