Intelligent Soft Robot Mobility in the Real World

这个项目的目标是创造柔软的，蛇形的机器人，可以在狭窄的空间，脆弱的物体，杂乱，粗糙和/或颗粒状表面的真实环境中导航。该项目包括三个研究重点，即分段蛇机器人的模块化机械设计，机器人执行简单功能运动原语的低级算法，以及构建适当的运动原语序列以满足任务目标的高级规划算法。由于软体机器人可以根据环境变化而变形，因此它们比刚性机器人更能适应意外情况。它们的高顺应性使软机器人具有安全有效地与障碍物交互的潜力，甚至可以使用周围的物体作为运动辅助。拥抱触摸而不是回避触摸的机器人可以通过人类与机器人的物理合作来改变应用。具体来说，直接使用辅助和智能三手设备或软辅助手臂可以无缝地为工人或残疾人提供新的能力。工业中的人机协作可以提高效率，降低成本，并增加美国制造业的全球竞争力。这个项目的目标是赋予软体机器人智能，在导航现实世界的环境中自主使用层次控制方法，从低级功能和运动原语到高级运动任务。为实现这一目标，需要完成的任务包括具有分布式驱动、高密度变形传感、嵌入式模块级控制和模块间通信的模块化软机器人体系结构；控制方法，实现可靠和可重复的动态模块响应，根据过去的迭代优化性能，并通过数据驱动建模，通过随机抽象和控制系统在其环境中提供高级决策；运动规划和运动算法，在混乱的环境中找到曲率有界的轨迹，利用接触实现障碍物辅助运动，以及3-D运动原语，在颗粒表面上操作。

The goal of this project is the creation of soft, snake-like robots that can navigate through real environments with confined spaces, fragile objects, clutter, rough and/or granular surfaces. The project includes three research thrusts, namely modular mechanical design of a segmented snake robot, low-level algorithms for the robot to execute simple functional motion primitives, and high-level planning algorithms to construct appropriate sequences of motion primitives in order to meet mission objectives. Because they can deform in response to their environment, soft robots can adapt to unexpected circumstances more robustly than a rigid robot. Their high compliance gives soft robots the potential to safely and effectively interact with obstacles, and even to use surrounding objects as aids to locomotion. Robots that embrace touch instead of avoiding it could transform applications through physical human-robot partnership. Specifically, the direct use of assistive and intelligent third-hand devices or soft supernumerary arms could seamlessly provide new capabilities to workers or the disabled. Human-robot collaboration in industry could improve efficiency, reduce cost, and increase the global competitiveness of US manufacturing. The objective of this project is to endow soft robots with intelligence in navigating real-world environments autonomously using a hierarchical control approach from low-level functions and motion primitives to high-level locomotion tasks. The tasks to be accomplished towards this objective include a modular soft robot architecture with distributed actuation, high-density deformation sensing, embedded module-level control, and inter-module communication; control methods to achieve reliable and repeatable dynamic module responses, to optimize performance over time based on past iterations, and to provide high-level decision making by stochastic abstraction and control of the system in its environment through data-driven modeling; and motion planning and locomotion algorithms to find curvature bounded trajectories through cluttered environments, exploit contact to achieve obstacle aided locomotion, and 3-D motion primitives to operate on granular surfaces.