Maximum Climbing
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Maximum Climbing
The definitive resource to brain-training for climbing—by an internationally recognized expert As physical as climbing is, it is even more mental. Ultimately, people climb with their minds—hands and feet are merely extensions of their thoughts and will. Becoming a master climber requires that you first master your mind. In Maximum Climbing, America’s best-selling author on climbing performance presents a climber’s guide to the software of the brain—one that will prove invaluable whether one's preference is bouldering, sport climbing, traditional climbing, alpine climbing, or mountaineering. Eric Hörst brings unprecedented clarity to the many cognitive and neurophysical aspects of climbing and dovetails this information into a complete program, setting forth three stages of mental training that correspond to beginner, intermediate, and elite levels of experience and commitment—the ideal template to build upon to personalize one's goals through years of climbing to come.
Training for Climbing
Drawing on new research in sports medicine, nutrition, and fitness, this book offers a training program to help any climber achieve superior performance and better mental concentration on the rock, with less risk of injury.
Bio-inspired Globally Convergent Gait Regulation for a Climbing Robot
Author: Salomon Joseph Trujillo
language: en
Publisher: Stanford University
Release Date: 2011
The priorities of a climbing legged robot are to maintain a grasp on its climbing surface and to climb efficiently against the force of gravity. Climbing robots are especially susceptible to thermal overload during normal operation, due to the need to oppose gravity and to frequently apply internal forces for clinging. These priorities guided us to develop optimal climbing behaviors under thermal constraints. These behaviors in turn profoundly constrain the choice of gait regulation methods. We propose a novel algorithm: "travel-based" gait regulation that varies foot detachment timing, effectively modifying stride length and frequency in order to maintain gait phasing, subject to kinematic and stability constraints. A core feature of the algorithm is "travel, " a new metric that plays a similar role to relative phasing. The method results in linear equations in terms of travel, leading to straightforward tests for local and global convergence when, for example, disturbances such as foot slippage cause departures from the nominal phasing. We form recurrence maps and use eigenvalue and singular value decomposition to examine local convergence of gaits. To examine global convergence, we implemented a computational geometry technique in high-order spaces. Our travel-based algorithm benefits from a compact code size and ease of implementation. We implemented the algorithm on the RiSE and Stickybot III robots as well as a virtual hexapod in a physics simulator. We demonstrated quickly converging gaits on all platforms as well as gait transitions on Stickybot III and the virtual hexapod.