Real-Time Motion Planning for Complex Robotic Tasks

复杂机器人任务的实时运动规划

• 批准号: RGPIN-2016-04156
• 负责人: Smith, Stephen
• 金额: $ 2.62万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708119
• 关键词: Real; Time; Motion; Planning; Complex

Robots are moving beyond the factory floor and into warehouses, private homes, commercial workplaces, hospitals, and the outdoors. This transition is being driven by rapid advances in robot technology. We are reaching a point where ground vehicles and aerial drones can be purchased at low cost. In addition, when the robot environment is known beforehand, capable algorithms exist for planning motion to perform complex tasks. However, real-world environments evolve in unpredictable ways. Humans may be moving in the environment, locations of obstacles may be unknown, and robots may not even be certain of their exact location. Such environments pose significant challenges. Robots can no longer fully plan their motion in advance, and they must react to environmental changes safely and in real-time. The long-term objective of the proposed research is to develop flexible robot motion planners that enable robots to efficiently complete complex motion tasks in the face of uncertainty. The driving idea behind this research program is to utilize extensive offline computation to determine motion strategies and contingencies, "preparing" the robot for the online and real-time execution. Online, we can leverage incremental motion planners to efficiently adapt robot motion. This research program will address several key research challenges related to planning under uncertainty. This will encompass methods for revising motion plans online, approaches for automatically determining what aspects of a robot mission can be planned independently, and techniques for planning when a robot is not even certain of its own location in its workspace. We will also tackle problems in finding efficient motion plans for robots with complex dynamics. A major barrier to the adoption of robotics is the initial investment required to develop efficient and safe robot motion controllers. This research will provide fundamental advances in robot motion planning that help reduce these costs. Worldwide, the robotics industry is expected to grow to $200 billion dollars annually by 2025. Canada's robotics industry is growing rapidly, and as these companies grow, they are transitioning their focus from building robot hardware to providing complete robotic solutions. The proposed research will lay the foundation on which general-purpose robots can be built, and from which complete robotic solutions can be offered. The program will produce highly qualified personnel in the rapidly-growing field of robotics. These students will have access to state-of-the-art robotics facilities at Waterloo, and will become experts both in developing robot autonomy and in working with robot hardware. This will position HQP at the forefront of a growing and transformative field.

机器人正在走出工厂车间，进入仓库、私人住宅、商业工作场所、医院和户外。这种转变是由机器人技术的快速发展推动的。我们正在达到一个点，地面车辆和空中无人机可以以低成本购买。此外，当机器人环境是已知的事先，有能力的算法存在规划运动，以执行复杂的任务。然而，现实世界的环境以不可预测的方式发展。 人类可能在环境中移动，障碍物的位置可能是未知的，机器人甚至可能不确定它们的确切位置。 这种环境带来了重大挑战。 机器人不再能够完全提前规划它们的运动，它们必须对环境变化做出安全和实时的反应。 该研究的长期目标是开发灵活的机器人运动规划器，使机器人能够有效地完成复杂的运动任务，面对不确定性。这项研究计划背后的驱动思想是利用广泛的离线计算来确定运动策略和突发事件，为机器人的在线和实时执行做好准备。在线上，我们可以利用增量运动规划器来有效地适应机器人运动。 该研究计划将解决与不确定性下的规划有关的几个关键研究挑战。这将包括在线修改运动计划的方法，自动确定机器人使命的哪些方面可以独立规划的方法，以及当机器人甚至不确定其在工作空间中的位置时进行规划的技术。我们还将解决为具有复杂动力学的机器人找到有效运动计划的问题。 采用机器人技术的一个主要障碍是开发高效安全的机器人运动控制器所需的初始投资。这项研究将为机器人运动规划提供根本性的进步，有助于降低这些成本。在全球范围内，机器人产业预计到2025年将增长到每年2000亿美元。加拿大的机器人产业正在迅速发展，随着这些公司的发展，他们正在将重点从制造机器人硬件转向提供完整的机器人解决方案。拟议的研究将奠定基础，在此基础上可以建立通用机器人，并从其中可以提供完整的机器人解决方案。该计划将在快速发展的机器人领域培养高素质的人才。这些学生将有机会获得国家的最先进的机器人设施在滑铁卢，并将成为专家都在开发机器人自主性和机器人硬件工作。这将使HQP处于一个不断发展和变革的领域的最前沿。