Achieving effective locomotion on diverse terrestrial substrates can require subtle changes of limb kinematics. Biologically inspired legged robots (physical models of organisms) have shown impressive mobility on hard ground but suffer performance loss on unconsolidated granular materials like sand. Because comprehensive limb-ground interaction models are lacking, optimal gaits on complex yielding terrain have been determined empirically. To develop predictive models for legged devices and to provide hypotheses for biological locomotors, we systematically study the performance of SandBot, a small legged robot, on granular media as a function of gait parameters. High performance occurs only in a small region of parameter space. A previously introduced kinematic model of the robot combined with a new anisotropic granular penetration force law predicts the speed. Performance on granular media is maximized when gait parameters minimize body acceleration and limb interference, and utilize solidification features of granular media.
A general locomotion control framework for multi-legged locomotors
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；National University of Singapore（新加坡国立大学）；Carnegie Mellon University（卡内基梅隆大学）；Morehouse College（莫尔豪斯学院）；Pennsylvania State University（宾夕法尼亚州立大学）；Rancho Research Institute（兰乔研究所）；Royal Veterinary College（皇家兽医学院）；University of Oviedo（奥维耶多大学）
Geometry of contact: contact planning for multi-legged robots via spin models duality
Georgia Institute of Technology（佐治亚理工学院）；NSF AI Institute for Artificial Intelligence and Fundamental Interactions（美国国家科学基金会人工智能与基本相互作用人工智能研究所）；Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
Learning to enhance multi-legged robot on rugged landscapes
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）；School of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning
Southern University of Science and Technology（南方科技大学）；School of Design（设计学院）；Department of Mechanical and Energy Engineering（机械与能源工程系）；Department of System Design and Intelligent Manufacturing（系统设计与智能制造系）
LIMBERO: A Limbed Climbing Exploration Robot Toward Traveling on Rocky Cliffs
Space Robotics Lab. (SRL) in Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（东京东京大学工学研究院航空航天工程系空间机器人实验室）；National Institute of Applied Sciences of Toulouse（图卢兹应用科学国家研究院）
Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots
ETH Zurich（苏黎世联邦理工学院）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bern（伯尔尼大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Lucerne University of Applied Sciences and Art（卢塞恩应用科学与艺术大学）
The Omega Turn: A General Turning Template for Elongate Robots
Pennsylvania State University（宾夕法尼亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Brown University（布朗大学）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；National University of Singapore（新加坡国立大学）；Oglethorpe University（奥格尔索普大学）
