Improved Autonomy for Unmanned Aerial Vehicles in Unstructured Environments

提高无人机在非结构化环境中的自主性

• 批准号: RGPIN-2017-06958
• 负责人: Lynch, Alan
• 金额: $ 2.4万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658153
• 关键词: Improved; Autonomy; Unmanned; Aerial; Vehicles

Global spending on unmanned aerial vehicles (UAV) is predicted to grow dramatically from USD 4 billion in 2015 to USD 14 billion by 2025, which is a total of USD 93 billion in a decade (source: Teal Group). Military research is expected to add USD 30 Billion in spending over this decade. This remarkable growth is because UAVs extend and complement human capability to do difficult jobs faster, more accurately, safer, and at a reduced budget. Civilian applications include disaster recovery in regions where humans cannot survive, or inspection of infrastructure such as electrical transmission lines covering vast expanses of inaccessible land. UAVs are part of a robotics revolution in the military where they are often used for intelligence, surveillance, and reconnaissance (ISR). They are key to the future of military forces worldwide, and in particular to that of Canada's Air Force as mentioned in strategic planning documents such as “Projecting Power Trends Shaping Canada's Air Force in the Year 2019” by the Canadian Forces Air Warfare Center.*******The aim of the proposed research is to improve UAV autonomy in unstructured environments; the work builds on the applicant's past successful UAV research program begun in 2004. We define autonomy broadly as capabilities achieved by a man-machine team where the UAV operates with some level of independence. Autonomous control for known environments (e.g., a mapped warehouse with beacons) cannot be applied to unstructured environments. There, robots must perceive their environment to make intelligent decisions towards a mission goal. First, we investigate new methods for vision-based navigation in an environment where GPS is degraded (e.g., due to adversarial jamming). An equally important case is navigation relative to an object with unknown GPS coordinate (e.g., grasping a payload). Although vision-based navigation has been used for visual servoing, it can be unnecessarily computationally intensive as it relies on continuous tracking of features and the maintenance of a map. Therefore, a second objective researches efficient visual servoing for specific autonomous motion control tasks including landing on a moving ship deck. Thirdly, we analyze and compensate for time delay in UAV control. Image processing required in visual servoing and navigation or long-range remote teleoperation introduces significant latency which limits performance. Fourthly, we investigate multi-vehicle coordinated control in general and its application to slung load transportation.*******The proposed research is important as it develops novel broadly applicable theory in the field of nonlinear control, visual servoing, visual navigation, and robotics. This theory is demonstrated on specific applications of practical importance. The benefits to Canada include innovation in the large and growing field of unmanned systems which are transforming military and civil applications.******

全球在无人机（UAV）上的支出预计将从2015年的40亿美元大幅增长到2025年的140亿美元，十年内总计将达到930亿美元（来源：Teal Group）。军事研究预计将在未来十年增加300亿美元的支出。这种显著的增长是因为无人机扩展和补充了人类更快、更准确、更安全、更少预算地完成困难工作的能力。民用应用包括在人类无法生存的地区进行灾难恢复，或检查覆盖大片人迹般的土地的输电线路等基础设施。无人机是军事机器人革命的一部分，它们经常用于情报、监视和侦察（ISR）。它们是全球军事力量未来的关键，特别是加拿大空军的关键，正如加拿大军队空战中心在“2019年塑造加拿大空军的预测力量趋势”等战略规划文件中所提到的那样。*******提出的研究目的是提高无人机在非结构化环境中的自主性；该工作建立在申请人在2004年开始的过去成功的无人机研究项目的基础上。我们将自主性广义地定义为由人机团队实现的能力，其中无人机具有一定程度的独立性。对已知环境（例如，带有信标的映射仓库）的自主控制不能应用于非结构化环境。在那里，机器人必须感知环境，才能朝着任务目标做出明智的决定。首先，我们研究了在GPS退化（例如，由于对抗性干扰）的环境中基于视觉的导航新方法。一个同样重要的情况是相对于GPS坐标未知的对象进行导航（例如，抓取有效载荷）。虽然基于视觉的导航已经被用于视觉伺服，但由于它依赖于特征的连续跟踪和地图的维护，它可能会不必要地计算密集。因此，第二个目标是研究针对特定自主运动控制任务的高效视觉伺服，包括在移动船舶甲板上着陆。第三，对无人机控制中的时滞进行了分析和补偿。在视觉伺服和导航或远程远程操作中需要的图像处理引入了显着的延迟，从而限制了性能。第四，研究了多车协调控制及其在吊装运输中的应用。*******本研究为非线性控制、视觉伺服、视觉导航和机器人等领域提供了新的、广泛适用的理论。这一理论在实际应用中得到了验证。对加拿大的好处包括在大规模和不断增长的无人系统领域的创新，这正在改变军事和民用应用。******