Maneuvering over Deformable Terrain: Long-horizon Task and Motion Planning of Bipedal Locomotion via Contact Sensing and Terrain Adaptation

这项拨款的主要目的是提高有腿机器人的规划能力。具体地说，该团队将为机器人配备必要的工具，以便在具有挑战性和不断变化的表面上行走，比如充满松散岩石和瓦砾的建筑工地。这项任务相当困难，因为有腿的机器人有很多运动部件，而且它们很难为如此复杂的地形规划自己的动作。当地面松软或不稳定时，它就变得更加具有挑战性，因为机器人必须保持平衡，并控制它如何与地面互动。为了实现这一目标，PI旨在使机器人能够提前规划它们的行动，并在要求苛刻的表面上行走时进行实时调整。他们正在从生物学中获得灵感，以设计更好的机器人，同时将不同的研究领域结合在一起，例如了解有腿的机器人的动力学，以及它们如何与表面接触，以及规划任务和运动。这项研究将培养不同的学生群体，包括佐治亚理工学院垂直整合计划的本科生，以及佐治亚理工学院通过工程与科学吸引新生代(Engages)计划的未被充分代表的高中生。此外，该奖项将支持将研究生水平的课程倡议纳入跨学科课程，以培训博士生在物理和机器人技术方面的接触，并扩大对计算、工程和科学学科的参与。如果成功，这项研究的结果可能会导致腿部机器人在各个领域的部署，包括建筑、农业和城市环境。最终，该项目的总体目标是增强两足机器人的能力，使它们能够在具有挑战性的地形中导航，并为各种现实世界的应用做出贡献，从而使我们的生活更轻松、更安全。在高度复杂和易变形的地形上进行长期、实时、多功能的两足机器人移动在很大程度上是未被探索的。这项研究旨在将可变形地形物理、自适应接触机构设计和多模式接触传感集成到一个统一的任务和运动规划框架中，使两足机器人能够在各种恶劣的地形上进行机动。特别是，该奖项将采用一种融合的方法，包括：(I)用于严酷地形分类和适应的多模式地形接触机制，(Ii)基于物理的颗粒状地形动力学建模和可变形贴片接触建模，以及(Iii)解决地形自适应分布式轨迹优化的长期规划算法，同时对多功能性步态和接触决策进行符号推理。PI将通过在不同时空尺度上广泛和具有代表性的机器人-地形相互作用实验来揭示基本的运动原理。这项研究将在以原则性方式将复杂的地形动力学和丰富的接触感应整合到两足动物运动规划和决策方面取得突破。这一奖项反映了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。

The main aim of this grant is to enhance the planning abilities of robots with legs. Specifically, the team will equip robots with the necessary tools to walk on challenging and ever-changing surfaces, like construction sites filled with loose rocks and debris. This task is quite difficult because robots with legs have many moving parts, and they struggle to plan their movements for such complex terrain. It becomes even more challenging when the ground is soft or unstable, as the robot must maintain its balance and control how it interacts with the surface. To achieve this goal, the PIs aim to enable robots to plan their actions in advance and adjust in real-time while walking on demanding surfaces. They are taking inspiration from biology to design better robots while bringing together different fields of study, such as understanding the dynamics of robots with legs and how they make contact with surfaces, as well as planning tasks and movements. This research will train a diverse group of students, including undergraduate students through the Georgia Tech Vertically Integrated Program, and underrepresented high-school students via the Engaging New Generation at Georgia Tech through Engineering & Science (ENGAGES) program. In addition, this award will support the inclusion of a graduate-level curriculum initiative integrated into a cross-disciplinary class to train PhD students at the interface of physics and robotics and broaden participation in computing, engineering, and science disciplines. If successful, the outcomes of this research could lead to the deployment of legged robots in various fields, including construction, agriculture, and urban environments. Ultimately, the project's overarching goal is to enhance the capabilities of bipedal robots, enabling them to navigate challenging terrains and contribute to various real-world applications, thereby making our lives easier and safer.Long-horizon, real-time, versatile bipedal robot locomotion over highly complex and deformable terrain is largely underexplored. This research aims to integrate deformable terrain physics, adaptive contact mechanism design, and multi-modal contact sensing into a unified task and motion planning framework and enable bipedal legged machines to maneuver over various harsh terrain. Particularly, this award will employ a convergent approach including: (i) multi-modal terrain contact mechanism for austere terrain classification and adaptation, (ii) physics-based granular terrain dynamics modeling and deformable patch contact modeling, and (iii) long-horizon planning algorithms solving terrain-adaptive distributed trajectory optimization while symbolically reasoning about versatile gaits and contact decisions. The PIs will uncover fundamental locomotion principles through extensive and representative robot-terrain interaction experiments at various spatiotemporal scales. This research will enable breakthroughs in the understanding of integrating complex terrain dynamics and rich contact sensing into bipedal locomotion planning and decision-making in a principled manner.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.