Heterogeneous Leg Stiffness and Roll in Dynamic Running

Legged robots are by nature strongly non-linear, high-dimensional systems whose full complexity permits neither tractable mathematical analysis nor comprehensive numerical study. In consequence, a growing body of literature interrogates simplified “template” [1], [2] models—to date almost exclusively confined to sagittal- or horizontal-plane motion—with the aim of gaining insight into the design or control of the far messier reality. In this paper we introduce a simple bounding-in-place (“BIP”) model as a candidate frontal plane template for straight-ahead level ground running and explore its use in formulating hypotheses about whether and why rolling motion is important in legged locomotion. Numerical study of left-right compliance asymmetry in the BIP model suggests that compliance ratios yielding lowest steady state roll suffer far longer disturbance recovery transients than those promoting greater steady state roll. We offer preliminary experimental data obtained from video motion capture data of the frontal plane disturbance recovery patterns of a RHex-like hexapod suggesting a correspondence to the conclusions of the numerical study. Fig. 1. EduBot [19], a RHex-like [20] hexapedal robot. Jonathan Clark was supported by the IC Postdoctoral Fellow Program under grant number HM158204-1-2030. Samuel Burden was supported by the SUNFEST REU program at the University of Pennsylvania. This work was also partially supported by the NSF FIBR grant #0425878. For more information: Kod*La

A Biomimetic Study of Discontinuous-Constraint Metamorphic Mechanism for Gecko-Like Robot

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A Self-Exciting Controller for High-Speed Vertical Running

Traditional legged runners and climbers have relied heavily on gait generators in the form of internal clocks or reference trajectories. In contrast, here we present physical experiments with a fast, dynamical, vertical wall climbing robot accompanying a stability proof for the controller that generates it without any need for an additional internal clock or reference signal. Specifically, we show that this “self-exciting” controller does indeed generate an “almost” globally asymptotically stable limit cycle: the attractor basin is as large as topologically possible and includes all the state space excluding a set with empty interior. We offer an empirical comparison of the resulting climbing behavior to that achieved by a more conventional clock-generated gait trajectory tracker. The new, self-exciting gait generator exhibits a marked improvement in vertical climbing speed, in fact setting a new benchmark in dynamic climbing by achieving a vertical speed of 1.5 body lengths per second. For more information: Kod*La

Morphological properties of mass-spring networks for optimal locomotion learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size

Biologically-plausible six-legged running : control and simulation

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 63-66).This thesis presents a controller which produces a stable, dynamic 1.4 meter per second run in a simulated twelve degree of freedom six-legged robot. The algorithm is relatively simple; it consists of only a few hand-tuned feedback loops and is defined by a total of 13 parameters. The control utilizes no vestibular-type inputs to actively control orientation. Evidence from perturbation, robustness, motion analysis, and parameter sensitivity tests indicate a high degree of stability in the simulated gait. The control approach generates a run with an aerial phase, utilizes force information to signal aerial phase leg retraction, has a forward running velocity determined by a single parameter, and couples stance and swing legs using angular momentum information. Both the hypotheses behind the control and the resulting gait are argued to be plausible models of biological locomotion.by Matthew David Malchano.M.Eng

Investigating Sensorimotor Control in Locomotion using Robots and Mathematical Models

Locomotion is a very diverse phenomenon that results from the interactions of a body and its environment and enables a body to move from one position to another. Underlying control principles rely among others on the generation of intrinsic body movements, adaptation and synchronization of those movements with the environment, and the generation of respective reaction forces that induce locomotion. We use mathematical and physical models, namely robots, to investigate how movement patterns emerge in a specific environment, and to what extent central and peripheral mechanisms contribute to movement generation. We explore insect walking, undulatory swimming and bimodal terrestrial and aquatic locomotion. We present relevant findings that explain the prevalence of tripod gaits for fast climbing based on the outcome of an optimization procedure. We also developed new control paradigms based on local sensory pressure feedback for anguilliform swimming, which include oscillator-free and decoupled control schemes, and a new design methodology to create physical models for locomotion investigation based on a salamander-like robot. The presented work includes additional relevant contributions to robotics, specifically a new fast dynamically stable walking gait for hexapedal robots and a decentralized scheme for highly modular control of lamprey-like undulatory swimming robots