18,601 research outputs found
Intelligent wheeled mobile robot navigation
The paper deals with the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and gives the fuzzy velocity control of a mobile robot motion in an unknown environment with obstacles. When the vehicle is moving towards the target and the sensors detect an obstacle, an avoiding strategy and velocity control are necessary. We proposed the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and a fuzzy reactive navigation strategy of collision-free motion and velocity control in an unknown environment with obstacles. The simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unknown environment. The proposed method have been implemented on the miniature mobile robot Khepera® that is equipped with sensors
A Novel Artificial Organic Controller with Hermite Optical Flow Feedback for Mobile Robot Navigation
This chapter describes a novel nature-inspired and intelligent control system for mobile robot navigation using a fuzzy-molecular inference (FMI) system as the control strategy and a single vision-based sensor device, that is, image acquisition system, as feedback. In particular, FMI system is proposed as a hybrid fuzzy inference system with an artificial hydrocarbon network structure as defuzzifier that deals with uncertainty in motion feedback, improving robot navigation in dynamic environments. Additionally, the robotics system uses processed information from an image acquisition device using a real-time Hermite optical flow approach. This organic and nature-inspired control strategy was compared with a conventional controller and validated in an educational robot platform, providing excellent results when navigating in dynamic environments with a single-constrained perception device
Mobile robot vavigation using a vision based approach
PhD ThesisThis study addresses the issue of vision based mobile robot navigation in a partially
cluttered indoor environment using a mapless navigation strategy. The work focuses on
two key problems, namely vision based obstacle avoidance and vision based reactive
navigation strategy.
The estimation of optical flow plays a key role in vision based obstacle avoidance
problems, however the current view is that this technique is too sensitive to noise and
distortion under real conditions. Accordingly, practical applications in real time robotics
remain scarce. This dissertation presents a novel methodology for vision based obstacle
avoidance, using a hybrid architecture. This integrates an appearance-based obstacle
detection method into an optical flow architecture based upon a behavioural control
strategy that includes a new arbitration module. This enhances the overall performance
of conventional optical flow based navigation systems, enabling a robot to successfully
move around without experiencing collisions.
Behaviour based approaches have become the dominant methodologies for designing
control strategies for robot navigation. Two different behaviour based navigation
architectures have been proposed for the second problem, using monocular vision as the
primary sensor and equipped with a 2-D range finder. Both utilize an accelerated
version of the Scale Invariant Feature Transform (SIFT) algorithm. The first
architecture employs a qualitative-based control algorithm to steer the robot towards a
goal whilst avoiding obstacles, whereas the second employs an intelligent control
framework. This allows the components of soft computing to be integrated into the
proposed SIFT-based navigation architecture, conserving the same set of behaviours
and system structure of the previously defined architecture. The intelligent framework
incorporates a novel distance estimation technique using the scale parameters obtained
from the SIFT algorithm. The technique employs scale parameters and a corresponding
zooming factor as inputs to train a neural network which results in the determination of
physical distance. Furthermore a fuzzy controller is designed and integrated into this
framework so as to estimate linear velocity, and a neural network based solution is
adopted to estimate the steering direction of the robot. As a result, this intelligent
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approach allows the robot to successfully complete its task in a smooth and robust
manner without experiencing collision.
MS Robotics Studio software was used to simulate the systems, and a modified Pioneer
3-DX mobile robot was used for real-time implementation. Several realistic scenarios
were developed and comprehensive experiments conducted to evaluate the performance
of the proposed navigation systems.
KEY WORDS: Mobile robot navigation using vision, Mapless navigation, Mobile
robot architecture, Distance estimation, Vision for obstacle avoidance, Scale Invariant
Feature Transforms, Intelligent framework
Mechatronic design and construction of an intelligent mobile robot for educational purposes : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Engineering and Automation at Massey University, Palmerston North, New Zealand
The main aim of this project was to produce a working intelligent mechatronically designed mobile robot, which could be used for educational purposes. A secondary aim was to make the robot as a test-bed to investigate new systems (sensors, control etc.) if possible. The mechatronic design of the robot was split in to three sections: the chassis, the sensors and the control. The design and construction of the chassis unit was relatively simple and very few problems were encountered. The drive system chosen for the robot was a four-wheeled Mecanum drive. The major advantage of this system is that it allows multiple degrees of freedom while keeping the control and the number of drive motors to a minimum. The design and construction of the sensors was the main research section. The sensor design evolved around the use of ultrasonic sensors. While a phased array type arrangement was tried with the intention of improving the angular accuracy of the sensors, the use of frequency modulation was used in the end and it proved to be excellent except that the problem of angular accuracy was still not solved. The entire mechatronic system was completed except for the micro controller programming. It operated well when it was given the correct inputs and performed all of the functions it was designed for. It is strongly recommended that further work be done on the use of a computer motherboard instead of the current micro controller as this would allow for easier programming, more complex programs and easy implementation of map building
Neural Networks in Mobile Robot Motion
This paper deals with a path planning and intelligent control of an
autonomous robot which should move safely in partially structured environment.
This environment may involve any number of obstacles of arbitrary shape and
size; some of them are allowed to move. We describe our approach to solving the
motion-planning problem in mobile robot control using neural networks-based
technique. Our method of the construction of a collision-free path for moving
robot among obstacles is based on two neural networks. The first neural network
is used to determine the "free" space using ultrasound range finder data. The
second neural network "finds" a safe direction for the next robot section of
the path in the workspace while avoiding the nearest obstacles. Simulation
examples of generated path with proposed techniques will be presented.Comment: 9 Page
A general learning co-evolution method to generalize autonomous robot navigation behavior
Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems
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
Design and implementation of a real-time autonomous navigation system applied to lego robots
Teaching theoretical concepts of a real-time autonomous robot system may be a challenging task without real hardware support. The paper discusses the application of the Lego Robot for teaching multi interdisciplinary subjects to Mechatronics students. A real-time mobile robot system with perception using sensors, path planning algorithm, PID controller is used as the case to demonstrate the teaching methodology. The novelties are introduced compared to classical robotic classes: (i) the adoption of a project-based learning approach as teaching methodology; (ii) an effective real-time autonomous navigation approach for the mobile robot. However, the extendibility and applicability of the presented approach are not limited to only the educational purpose
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