NRI: FND: Immersive whole-body teleoperation of wheeled humanoid robots for dynamic mobil manipulation

建筑和物流工人以及救灾、消防、警察、伐木和采矿等危险工作的工人都需要体力要求很高的劳动。在美国，这些工人中的许多人因工作场所事故而受伤、残疾或死亡。虽然机器人有潜力保护这些工人的福祉，但它们还不具备执行这些活动所固有的苛刻体力任务的能力。缺少的一个关键方面是直觉地协调他们的身体以放大他们对物体施加的力的智能，比如当人类自然地靠在一扇沉重的门上打开它的时候。该项目专注于开发将人类的控制智能与机器的体力和耐力相结合的技术。在这个框架中，可穿戴设备测量人类操作员的整个身体的姿势，以便远程的类人机器人可以同时复制相同的全身位置。这个项目的新奇之处在于使人类操作员能够同时感受到机器人对周围环境施加的力。例如，消防员通过亲自执行动作来命令机器人推动沉重的碎片，同时感觉机器人是否施加了足够的力而物体没有移动，或者它是否用力过大而失去平衡。这项研究的影响是通过将人类工人带离危险，同时利用他们的领域专业知识和运动智力来执行体力要求高的劳动，从而保护他们的福祉。这项研究促进将本科生和代表性不足的少数群体纳入研究，创建与K-12学生的外展活动，并在会议和大学活动中提出该系统的演示。该项目探索了一种新的双边遥操作框架，使人类操作员能够控制远程轮式类人机器人执行动态移动操纵(DMM)。DMM是一种结合了强力操控和灵活运动的体力任务。这一方案的关键假设是，通过全身触觉设备进行双侧遥操作，可以在非结构化的野外环境中实现安全和直观的DMM。为了验证这一假设，将在三年内实现三个具体目标。目标1：实现支持这项研究的硬件，包括(I)一个原型全身触觉设备，它读取操作员的姿势来控制远程机器人，并从机器人与其环境的交互中提供丰富的动觉、视觉和听觉反馈；(Ii)名为SATYRR的机器人，它结合了腿部系统、轮式运动和拟人化上半身的优点，用于双臂操作。目的2：研究DMM的全身双侧遥操作策略。设计人到机器人的运动映射，以实现直观的高带宽控制，并实施力反馈方案，即使在大冲击下也能向操作员提供可解释的反馈。目标3：使用共享自主性提高DMM的安全性。设计模型预测控制方案，尽可能地跟踪用户的输入，同时防止操作员输入会损害机器人的输入。为了评估规划的框架，将建立一个现实的实验室任务环境，其中包括障碍赛道，包括推动大型物体，打开受阻的门，在外部干扰下保持平衡，以及携带和投掷有效载荷。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Physically demanding labor is required of workers in construction and logistics, as well as dangerous jobs such as disaster relief, firefighting, policework, logging, and mining. Many of these workers in the US are injured, disabled, or die due to workplace incidents. Although robots have the potential to protect the well-being of these workers, they do not yet possess the capability to perform the demanding physical tasks which are inherent to these activities. A key missing aspect is the intelligence to intuitively coordinate their body to amplify the forces that they apply to objects, such as when humans naturally lean against a heavy door to open it. This project focuses on developing the technology to combine the control intelligence of humans with the physical strength and endurance of machines. In this framework, a wearable device measures the posture of the entire body of a human operator such that the remote human-like robot may reproduce the same whole-body position at the same time. The novelty of this project is to enable the human operator to simultaneously feel the forces that the robot applies to its surroundings. For instance, a firefighter commands the robot to push heavy debris by performing the motion herself, while simultaneously feeling whether the robot is exerting insufficient force and the object isn’t moving, or whether it is exerting excessive effort and losing balance. The impact of this research is the protection of the well-being of human workers by taking them away from danger, while leveraging their domain expertise and their motor intelligence to perform physically demanding labor. This research promotes the inclusion of undergraduate and underrepresented minorities in research, creates outreach activities with K-12 students, and proposes demonstrations of the system during conferences and University events.This project explores a novel bilateral teleoperation framework which enables a human operator to control a remote wheeled humanoid robot to perform Dynamic Mobile Manipulation (DMM). DMM are physical tasks which combine forceful manipulation and agile locomotion. The key hypothesis in this proposal is that bilateral teleoperation via a whole-body haptic device can achieve safe and intuitive DMM in unstructured field environments. To validate this hypothesis, three specific aims will be pursued over three years. Aim 1: Implement the hardware that enables this research, composed of (i) a prototype whole-body haptic device which reads the operator’s posture to control the remote robot, and provides rich kinesthetic, visual, and audial feedback from the robot’s interaction with its environment, and (ii) the robot named SATYRR, which combines advantages of legged systems, wheeled locomotion, and an anthropomorphic upper-body for dual-arm manipulation. Aim 2: Study whole-body bilateral teleoperation strategies for DMM. Devise a human-to-robot motion mapping for intuitive, high-bandwidth control, and implement force feedback schemes that deliver interpretable feedback to the operator even under large impacts. And Aim 3: Improve DMM safety using shared autonomy. Devise model predictive control schemes to follow the user's input as closely as possible while preventing operator inputs that would damage the robot. To evaluate the planned framework, a realistic laboratory task environment will be built with an obstacle course that includes pushing large objects, opening blocked doors, balancing under external disturbances, and carrying and throwing a payload.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.