Mechanical Intelligence of Locomotion and Intrusion in Slender Organisms in Terradynamically Rich Terrain

大多数生物体在复杂、异质和不可预测的环境中生存。虽然调控导航、生长和其他探索行为的基因和细胞网络各不相同，但不同的生命系统面临着共同的物理挑战。这表明，在生物体水平上，尽管潜在的生物机制存在巨大差异，但在生命系统中存在共同的控制策略（因此是运动的一般原则）。与对流体和空气动力学环境中运动的深入了解相反，生物体与复杂、异质的“丰富的地球动力学”环境相互作用的原理却鲜为人知。在这样的环境中，生物体对不断变化和不可预测的局部相互作用做出反应，而感官信息有限;相互作用的强度意味着生物体及其环境是高度耦合的，不能被视为独立的系统。物理动力学的中心地位和环境信息的不完整性表明，地球动力学丰富的环境运动既需要闭环的主动感觉反馈（通常与动物的神经控制或植物中的分散化学线索有关），也需要开环的被动控制和纯物理过程。这些后一种过程构成了一种机械智能，在这些过程中，身体与环境的相互作用被调整以产生适应性探索行为，而不需要主动反馈控制的帮助。在这个奖项中，研究团队试图发现主动闭环控制和被动机械智能控制机制在何处，何时以及如何相互作用，以在地球动力学丰富的环境中创造目标导向的生物体运动。 为此，他们将使用在物理环境中面临类似挑战的模型系统：1）O。sativa（rice）和A. thaliana根必须适应复杂的土壤环境; 2）C.在土壤和密集腐烂的植物组织中起伏的线虫; 3）在密集异质地形中的无肢机器人，例如在农业地区和搜索和救援行动中遇到的机器人。研究人员将研究运动学，力和遗传学负责有效的功能。更广泛地说，他们的发现可以深入了解机械和控制在进化和生物行为中的作用，同时，使我们能够开发出能够穿越自然环境的机器人，其性能与生命系统相当。所研究的生物和机器人系统也是大众感兴趣的自然主题，所获得的科学见解将用于教育和推广目的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Most organisms navigate complex, heterogeneous, and unpredictable environments to survive. While the genes and cellular networks that regulate navigation, growth, and other exploratory behaviors differ, diverse living systems face common physical challenges. This suggests the possibility that at the organismal level, common control strategies (and hence general principles of movement) exist across living systems despite dramatic differences in the underlying biological mechanisms. In contrast to insights gained into movement in hydro and aerodynamics environments, principles by which organisms interact with complex, heterogeneous “terradynamically rich” environments are less understood. In such environments, organisms respond to ever-changing and unpredictable local interactions with limited sensory information; the strength of the interactions implies that the organism and its environment are highly coupled and cannot be regarded as independent systems. The centrality of the physical dynamics and incompleteness of environmental information suggests that terradynamically rich environmental locomotion requires both closed-loop, active sensory feedback (commonly associated with neural control in animals or decentralized chemical cues in plants) and open-loop, passively controlled, and purely physical processes. These latter processes, in which body-environment interactions are tuned to produce adaptive exploratory behaviors without the aid of active feedback control, constitute a mechanical intelligence. In this award the team of investigators seek to discover where, when, and how active closed-loop control and passive, mechanically intelligent control mechanisms interact to create goal-oriented organism movement in terradynamically rich environments. To do so they will use model systems that face similar challenges within their physical environments: 1) O. sativa (rice) and A. thaliana roots which must navigate complex soil environments; 2) C. elegans nematodes which undulate in soil and dense, rotting vegetative tissue; 3) limbless robots in dense, heterogeneous terrains, such as those encountered in agricultural areas and search and rescue operations. The investigators will study the kinematics, forces and genetics responsible for effective function. More broadly, their findings can give insight into the role of mechanics and control in evolution and organismal behavior, and at the same time, allow us to develop robots which can traverse natural environments with performance comparable to living systems. The studied biological and robotic systems are also natural subjects of popular interest and the scientific insights gained will be leveraged for educational and outreach purposes.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.