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Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing

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TLDR
In this method, the self-localization of the robot is achieved by a model-based vision system, and nonstop navigation is realized by a retroactive position correction system.
Abstract
This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is achieved by a model-based vision system, and nonstop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We report on experiments in a hallway using the YAMABICO robot.

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Vision for mobile robot navigation: a survey

TL;DR: The developments of the last 20 years in the area of vision for mobile robot navigation are surveyed and the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment are discussed.
Journal ArticleDOI

A review of mobile robots: Concepts, methods, theoretical framework, and applications

TL;DR: The world of mobile robots is explored including the new trends led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human–robot interfaces, safe human-robot interaction, and emotion expression and perception.
Journal ArticleDOI

A Theoretical Foundation of Network Localization and Navigation

TL;DR: A theoretical foundation of NLN is presented, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy.
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Obstacle avoidance for kinematically redundant manipulators using a dual neural network

TL;DR: A recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability and an improved problem formulation is proposed that the collision-avoidance requirement is represented by dynamically-updated inequality constraints.
Journal ArticleDOI

A New Inequality-Based Obstacle-Avoidance MVN Scheme and Its Application to Redundant Robot Manipulators

TL;DR: Experimental results of the proposed MVN scheme implemented on the practical six-link planar robot manipulator substantiate the physical realizability and effectiveness of such a scheme for obstacle avoidance of redundant robot manipulators.
References
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Journal ArticleDOI

Mobile robot localization by tracking geometric beacons

TL;DR: An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
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Real-time obstacle avoidance for fast mobile robots

TL;DR: A real-time obstacle avoidance approach for mobile robots that permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advance toward the target.
Journal ArticleDOI

Inertial navigation systems for mobile robots

TL;DR: A low-cost solid-state inertial navigation system for mobile robotics applications is described and error models for the inertial sensors are generated and included in an extended Kalman filter for estimating the position and orientation of a moving robot vehicle.
Journal ArticleDOI

Dynamic map building for an autonomous mobile robot

TL;DR: An algorithm for autonomous map building and maintenance for a mobile robot that combines a location estimate and two distinct measures of uncertainty: a covariance matrix to represent uncertainty in feature loca tion, and a credibility measure to represent belief in the validity of the feature.
Journal ArticleDOI

Histogramic in-motion mapping for mobile robot obstacle avoidance

TL;DR: HIMM has been implemented and tested on a mobile robot and its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78 m/s.
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