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Mobile robot navigation

About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.


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Proceedings ArticleDOI
25 Mar 1985
TL;DR: A system which performs task-oriented navigation for an intelligent mobile robot is described in this paper, based on a dynamically maintained model of the local environment, called the "Composite Local Model."
Abstract: A system which performs task-oriented navigation for an intelligent mobile robot is described in this paper. This navigation system is based on a dynamically maintained model of the local environment, called the "Composite Local Model." The Composite Local Model integrates information from a rotating sonar sensor, the robot's touch sensor and a pre-learned Global Model as the robot moves through its environment. Techniques are described for constructing a line segment description of the most recent sensor scan (the Sensor Model), and for integrating such descriptions to build up a model of the immediate environment (the Composite Local Model). Model integration is based on a process of reinforcing the confidence in consistent information while decaying the confidence in inconsistent information. The estimated position of the robot is corrected by the difference in position between observed sensor signals and the corresponding symbols in the Composite Local Model. This system is useful for navigation in a finite, pre-learned domain such as a house, office, or factory.

106 citations

DissertationDOI
10 Dec 2002
TL;DR: This paper considers the problem of a robot navigating in a crowded or congested environment and proposes a hierarchical representation of POMDPs to attempt to predict the motion trajectory of humans and obstacles.
Abstract: This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-term and long-term. The short-term prediction refers to the one-step ahead prediction and the long-term to the prediction of the final destination point of the obstacle's movement. The robot movement is controlled by a partially observable Markov decision process (POMDP). POMDPs are utilized because of their ability to model information about the robot's location and sensory information in a probabilistic manner. The solution of a POMDP is computationally expensive and thus a hierarchical representation of POMDPs is used.

106 citations

Journal ArticleDOI
TL;DR: A low-cost robot capable of playing ping-pong against human opponent using a vision system to detect the ball, thus reducing the computational time and hardware requirements is presented.
Abstract: In this paper, a low-cost robot capable of playing ping-pong against human opponent using a vision system to detect the ball is presented. In the subsequent sections, the three main subsystems of the robot, i.e., the vision system, mechanical structure, and the control systems, are described. A prototype has been designed with lightweight and resistant materials to increase the response time and accurateness of the shot. One of the important features of this system is that it uses only one camera to detect the ball, thus reducing the computational time and hardware requirements. To detect the location of the ball, the robot combines the information about the ball and about the shadow it casts on the table. The expert module control defines the game strategy. Orienting the bat in order to return the ball to the desired position on the table does this. In these experiments, the success rate in returning balls was greater than 80%.

106 citations

Journal ArticleDOI
TL;DR: A singleton type-1 fuzzy logic system (T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment is introduced.

106 citations

01 Jan 2003
TL;DR: In this article, the application of a sensor network to navigate a flying robot is introduced. But the robot itself is an integral part of the localization process which establishes the positions of sensors which are not known a priori.
Abstract: This paper introduces the application of a sensor network to navigate a flying robot. We have developed distributed algorithms and efficient geographic routing techniques to incrementally guide one or more robots to points of interest based on sensor gradient fields, or along paths defined in terms of Cartesian coordinates. The robot itself is an integral part of the localization process which establishes the positions of sensors which are not known a priori. We use this system in a large-scale outdoor experiment with Mote sensors to guide an autonomous helicopter along a path encoded in the network.

106 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202358
2022179
202194
2020125
2019146
2018129