5,881 research outputs found
The Problem of Adhesion Methods and Locomotion Mechanism Development for Wall-Climbing Robots
This review considers a problem in the development of mobile robot adhesion
methods with vertical surfaces and the appropriate locomotion mechanism design.
The evolution of adhesion methods for wall-climbing robots (based on friction,
magnetic forces, air pressure, electrostatic adhesion, molecular forces,
rheological properties of fluids and their combinations) and their locomotion
principles (wheeled, tracked, walking, sliding framed and hybrid) is studied.
Wall-climbing robots are classified according to the applications, adhesion
methods and locomotion mechanisms. The advantages and disadvantages of various
adhesion methods and locomotion mechanisms are analyzed in terms of mobility,
noiselessness, autonomy and energy efficiency. Focus is placed on the physical
and technical aspects of the adhesion methods and the possibility of combining
adhesion and locomotion methods
A Model of Operant Conditioning for Adaptive Obstacle Avoidance
We have recently introduced a self-organizing adaptive neural controller that learns to control movements of a wheeled mobile robot toward stationary or moving targets, even when the robot's kinematics arc unknown, or when they change unexpectedly during operation. The model has been shown to outperform other traditional controllers, especially in noisy environments. This article describes a neural network module for obstacle avoidance that complements our previous work. The obstacle avoidance module is based on a model of classical and operant conditioning first proposed by Grossberg ( 1971). This module learns the patterns of ultrasonic sensor activation that predict collisions as the robot navigates in an unknown cluttered environment. Along with our original low-level controller, this work illustrates the potential of applying biologically inspired neural networks to the areas of adaptive robotics and control.Office of Naval Research (N00014-95-1-0409, Young Investigator Award
Mobile Robot Lab Project to Introduce Engineering Students to Fault Diagnosis in Mechatronic Systems
This document is a self-archiving copy of the accepted version of the paper.
Please find the final published version in IEEEXplore: http://dx.doi.org/10.1109/TE.2014.2358551This paper proposes lab work for learning fault detection and diagnosis (FDD) in mechatronic systems. These skills are important for engineering education because FDD is a key capability of competitive processes and products. The intended outcome of the lab work is that students become aware of the importance of faulty conditions and learn to design FDD strategies for a real system. To this end, the paper proposes a lab project where students are requested to develop a discrete event dynamic system (DEDS) diagnosis to cope with two faulty conditions in an autonomous mobile robot task. A sample solution is discussed for LEGO Mindstorms NXT robots with LabVIEW. This innovative practice is relevant to higher education engineering courses related to mechatronics, robotics, or DEDS. Results are also given of the application of this strategy as part of a postgraduate course on fault-tolerant mechatronic systems.This work was supported in part by the Spanish CICYT under Project DPI2011-22443
COCrIP: Compliant OmniCrawler In-pipeline Robot
This paper presents a modular in-pipeline climbing robot with a novel
compliant foldable OmniCrawler mechanism. The circular cross-section of the
OmniCrawler module enables a holonomic motion to facilitate the alignment of
the robot in the direction of bends. Additionally, the crawler mechanism
provides a fair amount of traction, even on slippery surfaces. These advantages
of crawler modules have been further supplemented by incorporating active
compliance in the module itself which helps to negotiate sharp bends in small
diameter pipes. The robot has a series of 3 such compliant foldable modules
interconnected by the links via passive joints. For the desirable pipe diameter
and curvature of the bends, the spring stiffness value for each passive joint
is determined by formulating a constrained optimization problem using the
quasi-static model of the robot. Moreover, a minimum friction coefficient value
between the module-pipe surface which can be vertically climbed by the robot
without slipping is estimated. The numerical simulation results have further
been validated by experiments on real robot prototype
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