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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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Book
Russell L. Anderson1
01 Jan 1988
TL;DR: The first real-time robot ping-pong player was described in this paper, where the first robot was able to play, and even beat, human pingpong players.
Abstract: This tour de force in experimental robotics paves the way toward understanding dynamic environments in vision and robotics. It describes the first robot able to play, and even beat, human ping-pong players.Constructing a machine to play ping-pong was proposed years ago as a particularly difficult problem requiring fast, accurate sensing and actuation, and the intelligence to play the game. The research reported here began as a series of experiments in building a true real-time vision system. The ping-pong machine incorporates sensor and processing techniques as well as the techniques needed to intelligently plan the robot's response in the fraction of a second available. it thrives on a constant stream of new data. Subjectively evaluating and improving its motion plan as the data arrives, it presages future robot systems with many joints and sensors that must do the same, no matter what the task.Contents: Introduction. Robot Ping-Pong. System Design. Real-Time Vision System Robot Controller. Expert Controller Preliminaries. Expert Controller. Robot Ping-Pong Application. Conclusion.Russell L. Andersson is Member of Technical Staff, Robotics Systems Research Department, AT&T Bell Laboratories. "A Robot Ping-Pong Player" is included in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.

177 citations

Book
04 Dec 2009
TL;DR: The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm.
Abstract: Stefan Bernard Williams Doctor of Philosophy The University of Sydney September 2001 Efficient Solutions to Autonomous Mapping and Navigation Problems This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n3) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n2) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the

176 citations

Journal ArticleDOI
TL;DR: A new technique for vision-based robot navigation that can calculate the robot position with variable accuracy (‘hierarchical localisation’) saving computational time when the robot does not need a precise localisation (e.g. when it is travelling through a clear space).

175 citations

Journal ArticleDOI
23 May 2017
TL;DR: The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments.
Abstract: Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor actuator control techniques The applications of the autonomous mobile robot in many fields such as industry space defence and transportation and other social sectors are growing day by day The mobile robot performs many tasks such as rescue operation patrolling disaster relief planetary exploration and material handling etc Therefore an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance The present article focuses on the study of the intelligent navigation techniques which are capable of navigating a mobile robot autonomously in static as well as dynamic environments

175 citations

Book ChapterDOI
01 Jul 1990
TL;DR: The main purpose of this project was “to study processes for the realtime control of a robot system that interacts with a complex environment” 〈NIL 69〉.
Abstract: Research on mobile robots began in the late sixties with the Stanford Research Institute’s pioneering work. Two versions of SHAKEY, an autonomous mobile robot, were built in 1968 and 1971. The main purpose of this project was “to study processes for the realtime control of a robot system that interacts with a complex environment” 〈NIL 69〉. Indeed, mobile robots were and still are a very convenient and powerful support for research on artificial intelligence oriented robotics. They possess the capacity to provide a variety of problems at different levels of generality and difficulty in a large domain including perception, decision making, communication, etc., which all have to be considered within the scope of the specific constraints of robotics: on-line computing, cost considerations, operating ability, and reliability.

175 citations


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