Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Design of a Biomimetic Mechanical Leg and Accompanying Sensor System for Terrain Detection

Autonomous robots are useful in a wide range of applications. However, finding a balance between speed and stability in an autonomous robot can be difficult. The goal of this project was to design a biomimetically-inspired robotic leg and accompanying sensor system for detecting terrain; the mechanical leg and sensor system designs in combination are intended to enable a quadruped robot to move quickly while maintaining its stability. In order to accomplish this goal, a leg was designed based on the leg of a cheetah and the team performed a variety of mechanical analyses on it. Additionally, the output from a force sensor landing on hard and muddy surfaces was collected and algorithms for determining which of the two surfaces the robot was walking on were developed

Multistable Phase Regulation for Robust Steady and Transitional Legged Gaits

We develop robust methods that allow specification, control, and transition of a multi-legged robot’s stepping pattern—its gait—during active locomotion over natural terrain. Resulting gaits emerge through the introduction of controllers that impose appropriately-placed repellors within the space of gaits, the torus of relative leg phases, thereby mitigating against dangerous patterns of leg timing. Moreover, these repellors are organized with respect to a natural cellular decomposition of gait space and result in limit cycles with associated basins that are well characterized by these cells, thus conferring a symbolic character upon the overall behavioral repertoire. These ideas are particularly applicable to four- and six-legged robots, for which a large variety of interesting and useful (and, in many cases, familiar) gaits exist, and whose tradeoffs between speed and reliability motivate the desire for transitioning between them during active locomotion. We provide an empirical instance of this gait regulation scheme by application to a climbing hexapod, whose “physical layer” sensor-feedback control requires adequate grasp of a climbing surface but whose closed loop control perturbs the robot from its desired gait. We document how the regulation scheme secures the desired gait and permits operator selection of different gaits as required during active climbing on challenging surfaces

Characterization of Dynamic Behaviors in a Hexapod Robot

This paper investigates the relationship between energetic effi- ciency and the dynamical structure of a legged robot’s gait. We present an experimental data set collected from an untethered dynamic hexapod, EduBot [1] (a RHex-class [2] machine), operating in four distinct manually selected gaits. We study the robot’s single tripod stance dynamics of the robot which are identified by a purely jointspace-driven estimation method introduced in this paper. Our results establish a strong relationship between energetic efficiency (simultaneous reduction in power consumption and in- crease in speed) and the dynamical structure of an alternating tripod gait as measured by its fidelity to the SLIP mechanics—a dynamical pattern exhibit- ing characteristic exchanges of kinetic and spring-like potential energy [3]. We conclude that gaits that are dynamic in this manner give rise to better uti- lization of energy for the purposes of locomotion. This work is supported in part by the National Science Foundation (NSF) under a FIBR Award 0425878. Yasemin Ozkan Aydin is supported by International Research Fellowship Programme of the Scientific and Technological Research Council of Turkey (TUBITAK). For more information: Kod*La

Sensitive dependence of the motion of a legged robot on granular media

Legged locomotion on flowing ground ({\em e.g.} granular media) is unlike locomotion on hard ground because feet experience both solid- and fluid-like forces during surface penetration. Recent bio-inspired legged robots display speed relative to body size on hard ground comparable to high performing organisms like cockroaches but suffer significant performance loss on flowing materials like sand. In laboratory experiments we study the performance (speed) of a small (2.3 kg) six-legged robot, SandBot, as it runs on a bed of granular media (1 mm poppy seeds). For an alternating tripod gait on the granular bed, standard gait control parameters achieve speeds at best two orders of magnitude smaller than the 2 body lengths/s ($\approx 60$ cm/s) for motion on hard ground. However, empirical adjustment of these control parameters away from the hard ground settings, restores good performance, yielding top speeds of 30 cm/s. Robot speed depends sensitively on the packing fraction $\phi$ and the limb frequency $\omega$, and a dramatic transition from rotary walking to slow swimming occurs when $\phi$ becomes small enough and/or $\omega$ large enough. We propose a kinematic model of the rotary walking mode based on generic features of penetration and slip of a curved limb in granular media. The model captures the dependence of robot speed on limb frequency and the transition between walking and swimming modes but highlights the need for a deeper understanding of the physics of granular media.Comment: 4 figure

Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda

Die vorliegende Dissertation mit dem Titel “Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda” untersucht in drei Studien exemplarisch, wie (i) Wüstenameisen ihre Beine einsetzen um An- und Abstiege zu überwinden, wie (ii) Wüsten- und Waldameisen ein Umkippen an steilen Anstiegen vermeiden, und wie sich (iii) Madagaskar-Fauchschaben, Amerikanische Großschaben und Blaberus discoidalis Audinet-Servill, 1839 aus Rückenlagen drehen und aufrichten. Neuartige biomechanischen Beschreibungen umfassen unter anderem: Impuls- und Kraftwirkungen einzelner Ameisenbeine auf den Untergrund beim Bergauf- und Bergabklettern, Kippmomente bei kletternden Ameisen, Energiegebirge-Modelle (energy landscapes) zur Quantifizierung der Körperform für die funktionelle Beschreibung des Umdrehens aus der Rückenlage

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks