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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.


Papers
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Book ChapterDOI
13 Sep 2004
TL;DR: The study found that the effects of adding landmark information to basic pedestrian navigation instructions (i.e. those which included distance to turn and street name only) replicate that for vehicle navigation systems.
Abstract: The beneficial effects of using landmarks in vehicle navigation systems (improved user confidence and navigation performance) have been well-studied and proven. The study reported here aimed to investigate the effects of adding landmark information to basic pedestrian navigation instructions (i.e. those which included distance to turn and street name only). The study found that the results replicate that for vehicle navigation systems. User confidence was raised to a consistently high level as a result of landmark inclusion and errors were greatly reduced. The results also indicate the types of manoeuvre that should benefit most from the inclusion of landmarks.

75 citations

Journal ArticleDOI
TL;DR: A neural dynamics approach is proposed for complete area coverage navigation by multiple robots using a bioinspired neural network to model the workspace and guide a swarm of robots for the coverage mission.
Abstract: Multiple robots collaboratively achieve a common coverage goal efficiently, which can improve work capacity, share coverage tasks, and reduce completion time. In this paper, a neural dynamics (ND) approach is proposed for complete area coverage navigation by multiple robots. A bioinspired neural network (NN) is designed to model the workspace and guide a swarm of robots for the coverage mission. The dynamics of each neuron in the topologically organized NN is characterized by an ND equation. Each mobile robot regards other robots as moving obstacles. Each robot path is autonomously generated from the neural activity landscape of the NN and the previous robot position. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation, comparison studies, and experiments.

75 citations

01 Jan 2000
TL;DR: An overview of issues regarding the design of the human machine interface for GPS-based vehicle navigation systems is presented followed by a look at the three most important interfaces: voice display, and control.
Abstract: In this paper, the author presents an overview of issues regarding the design of the human machine interface (HMI) for GPS-based vehicle navigation systems. General usability issues are first covered followed by a look at the three most important interfaces: voice display, and control.

75 citations

Journal ArticleDOI
TL;DR: The cooperative navigation system (CNS) algorithm described here is based on a Kalman filter which uses inter-robot position sensing to update the collective position estimates of the group.
Abstract: The navigation capability of a group of robots can be improved by sensing of relative inter-robot positions and intercommunication of position estimates and planned trajectories. The cooperative navigation system (CNS) algorithm described here is based on a Kalman filter which uses inter-robot position sensing to update the collective position estimates of the group. Assuming independence of sensing and positioning errors, the CNS algorithm always improves individual robot estimates and the collective navigation performance improves as the number of robots increases. The CNS algorithm computation may be distributed among the robot group. Simulation results and experimental measurements on two Yamabico robots are described.

75 citations

Proceedings Article
09 Jul 2016
TL;DR: The verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans is introduced and an algorithm for segmenting a path and mapping each segment to an utterance is presented.
Abstract: Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars.

75 citations


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