Mobile Robot Localisation and Navigation Using LEGO NXT and Ultrasonic Sensor Article Swipe
Yanan Liu
,
Rui Fan
,
Bin Yu
,
Mohammud Junaid Bocus
,
Ming Liu
,
Hepeng Ni
,
Jiahe Fan
,
Shixin Mao
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1810.08816
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1810.08816
Mobile robots are becoming increasingly important both for individuals and industries. Mobile robotic technology is not only utilised by experts in this field but is also very popular among amateurs. However, implementing a mobile robot to perform tasks autonomously can be expensive because of the need for various types of sensors and the high price of robot platforms. Hence, in this paper we present a mobile robot localisation and navigation system which uses a LEGO ultrasonic sensor in an indoor map based on the LEGO MINDSTORM NXT. This provides an affordable and ready-to-use option for most robot fans. In this paper, an effective method is proposed to extract useful information from the distorted readings collected by the ultrasonic sensor. Then, the particle filter is used to localise the robot. After robot's position is estimated, a sampling-based path planning method is proposed for the robot navigation. This method reduces the robot accumulative motion error by minimising robot turning times and covering distances. The robot localisation and navigation algorithms are implemented in MATLAB. Simulation results show an average accuracy between 1 and 3 cm for three different indoor map locations. Furthermore, experiments performed in a real setup show the effectiveness of the proposed methods.
Related Topics
Concepts
Mobile robot
Mobile robot navigation
Robot
Ultrasonic sensor
Particle filter
Artificial intelligence
Computer science
Motion planning
Monte Carlo localization
Computer vision
Real-time computing
Robot control
Simulation
Filter (signal processing)
Acoustics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1810.08816
- https://arxiv.org/pdf/1810.08816
- OA Status
- green
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2951106812
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2951106812Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1810.08816Digital Object Identifier
- Title
-
Mobile Robot Localisation and Navigation Using LEGO NXT and Ultrasonic SensorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-20Full publication date if available
- Authors
-
Yanan Liu, Rui Fan, Bin Yu, Mohammud Junaid Bocus, Ming Liu, Hepeng Ni, Jiahe Fan, Shixin MaoList of authors in order
- Landing page
-
https://arxiv.org/abs/1810.08816Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1810.08816Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1810.08816Direct OA link when available
- Concepts
-
Mobile robot, Mobile robot navigation, Robot, Ultrasonic sensor, Particle filter, Artificial intelligence, Computer science, Motion planning, Monte Carlo localization, Computer vision, Real-time computing, Robot control, Simulation, Filter (signal processing), Acoustics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.very | 26 |
| abstract_inverted_index.After | 129 |
| abstract_inverted_index.Then, | 119 |
| abstract_inverted_index.among | 28 |
| abstract_inverted_index.based | 81 |
| abstract_inverted_index.error | 152 |
| abstract_inverted_index.fans. | 97 |
| abstract_inverted_index.field | 22 |
| abstract_inverted_index.paper | 61 |
| abstract_inverted_index.price | 54 |
| abstract_inverted_index.robot | 34, 56, 66, 96, 143, 149, 155, 162 |
| abstract_inverted_index.setup | 194 |
| abstract_inverted_index.tasks | 37 |
| abstract_inverted_index.three | 183 |
| abstract_inverted_index.times | 157 |
| abstract_inverted_index.types | 48 |
| abstract_inverted_index.which | 71 |
| abstract_inverted_index.Hence, | 58 |
| abstract_inverted_index.Mobile | 0, 11 |
| abstract_inverted_index.filter | 122 |
| abstract_inverted_index.indoor | 79, 185 |
| abstract_inverted_index.method | 103, 138, 146 |
| abstract_inverted_index.mobile | 33, 65 |
| abstract_inverted_index.motion | 151 |
| abstract_inverted_index.option | 93 |
| abstract_inverted_index.paper, | 100 |
| abstract_inverted_index.robot. | 128 |
| abstract_inverted_index.robots | 1 |
| abstract_inverted_index.sensor | 76 |
| abstract_inverted_index.system | 70 |
| abstract_inverted_index.useful | 108 |
| abstract_inverted_index.MATLAB. | 170 |
| abstract_inverted_index.average | 175 |
| abstract_inverted_index.because | 42 |
| abstract_inverted_index.between | 177 |
| abstract_inverted_index.experts | 19 |
| abstract_inverted_index.extract | 107 |
| abstract_inverted_index.perform | 36 |
| abstract_inverted_index.popular | 27 |
| abstract_inverted_index.present | 63 |
| abstract_inverted_index.reduces | 147 |
| abstract_inverted_index.results | 172 |
| abstract_inverted_index.robot's | 130 |
| abstract_inverted_index.robotic | 12 |
| abstract_inverted_index.sensor. | 118 |
| abstract_inverted_index.sensors | 50 |
| abstract_inverted_index.turning | 156 |
| abstract_inverted_index.various | 47 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.accuracy | 176 |
| abstract_inverted_index.becoming | 3 |
| abstract_inverted_index.covering | 159 |
| abstract_inverted_index.localise | 126 |
| abstract_inverted_index.methods. | 201 |
| abstract_inverted_index.particle | 121 |
| abstract_inverted_index.planning | 137 |
| abstract_inverted_index.position | 131 |
| abstract_inverted_index.proposed | 105, 140, 200 |
| abstract_inverted_index.provides | 88 |
| abstract_inverted_index.readings | 113 |
| abstract_inverted_index.utilised | 17 |
| abstract_inverted_index.MINDSTORM | 85 |
| abstract_inverted_index.amateurs. | 29 |
| abstract_inverted_index.collected | 114 |
| abstract_inverted_index.different | 184 |
| abstract_inverted_index.distorted | 112 |
| abstract_inverted_index.effective | 102 |
| abstract_inverted_index.expensive | 41 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.performed | 190 |
| abstract_inverted_index.Simulation | 171 |
| abstract_inverted_index.affordable | 90 |
| abstract_inverted_index.algorithms | 166 |
| abstract_inverted_index.distances. | 160 |
| abstract_inverted_index.estimated, | 133 |
| abstract_inverted_index.locations. | 187 |
| abstract_inverted_index.minimising | 154 |
| abstract_inverted_index.navigation | 69, 165 |
| abstract_inverted_index.platforms. | 57 |
| abstract_inverted_index.technology | 13 |
| abstract_inverted_index.ultrasonic | 75, 117 |
| abstract_inverted_index.experiments | 189 |
| abstract_inverted_index.implemented | 168 |
| abstract_inverted_index.individuals | 8 |
| abstract_inverted_index.industries. | 10 |
| abstract_inverted_index.information | 109 |
| abstract_inverted_index.navigation. | 144 |
| abstract_inverted_index.Furthermore, | 188 |
| abstract_inverted_index.accumulative | 150 |
| abstract_inverted_index.autonomously | 38 |
| abstract_inverted_index.implementing | 31 |
| abstract_inverted_index.increasingly | 4 |
| abstract_inverted_index.localisation | 67, 163 |
| abstract_inverted_index.ready-to-use | 92 |
| abstract_inverted_index.effectiveness | 197 |
| abstract_inverted_index.sampling-based | 135 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |