Intelligent Soft Robot Mobility in the Real World

该项目的目标是创造柔软的蛇形机器人，能够在真实环境中导航，这些环境中有受限的空间、脆弱的物体、杂乱的、粗糙的和/或颗粒状的表面。该项目包括三个研究方向，即分段式蛇形机器人的模块化机械设计，机器人执行简单功能运动基元的底层算法，以及构建合适的运动基元序列以满足任务目标的高层规划算法。因为软体机器人可以随着环境的变化而变形，所以它们比刚性机器人更能适应意想不到的环境。它们的高度顺应性使软机器人有可能安全有效地与障碍物互动，甚至可以利用周围的物体作为移动的辅助工具。拥抱而不是回避触摸的机器人可以通过人与机器人的物理伙伴关系来改变应用程序。具体地说，直接使用辅助和智能的第三手设备或软编外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-机器人协作可以提高效率，降低成本，并增加美国制造业的全球竞争力。该项目的目标是赋予软机器人智能，使用从低层功能和运动基元到高级运动任务的分级控制方法，自主地在真实环境中导航。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；实现可靠和可重复的动态模块响应的控制方法；基于过去迭代的随着时间的推移优化性能的控制方法；通过数据驱动建模对环境中的系统进行随机抽象和控制来提供高层决策；运动规划和运动算法，在杂乱的环境中发现曲率有界的轨迹，利用接触来实现障碍辅助移动，以及在颗粒表面上操作的三维运动基元。

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.