Hand–Eye Separation-Based First-Frame Positioning and Follower Tracking Method for Perforating Robotic Arm Article Swipe
Handuo Zhang
,
Jun Guo
,
Chunyan Xu
,
Bin Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15052769
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15052769
In subway tunnel construction, current hand–eye integrated drilling robots use a camera mounted on the drilling arm for image acquisition. However, dust interference and long-distance operation cause a decline in image quality, affecting the stability and accuracy of the visual recognition system. Additionally, the computational complexity of high-precision detection models limits deployment on resource-constrained edge devices, such as industrial controllers. To address these challenges, this paper proposes a dual-arm tunnel drilling robot system with hand–eye separation, utilizing the first-frame localization and follower tracking method. The vision arm (“eye”) provides real-time position data to the drilling arm (“hand”), ensuring accurate and efficient operation. The study employs an RFBNet model for initial frame localization, replacing the original VGG16 backbone with ShuffleNet V2. This reduces model parameters by 30% (135.5 MB vs. 146.3 MB) through channel splitting and depthwise separable convolutions to reduce computational complexity. Additionally, the GIoU loss function is introduced to replace the traditional IoU, further optimizing bounding box regression through the calculation of the minimum enclosing box. This resolves the gradient vanishing problem in traditional IoU and improves average precision (AP) by 3.3% (from 0.91 to 0.94). For continuous tracking, a SiamRPN-based algorithm combined with Kalman filtering and PID control ensures robustness against occlusions and nonlinear disturbances, increasing the success rate by 1.6% (0.639 vs. 0.629). Experimental results show that this approach significantly improves tracking accuracy and operational stability, achieving 31 FPS inference speed on edge devices and providing a deployable solution for tunnel construction’s safety and efficiency needs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15052769
- https://www.mdpi.com/2076-3417/15/5/2769/pdf?version=1741096546
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408150734
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408150734Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15052769Digital Object Identifier
- Title
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Hand–Eye Separation-Based First-Frame Positioning and Follower Tracking Method for Perforating Robotic ArmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-04Full publication date if available
- Authors
-
Handuo Zhang, Jun Guo, Chunyan Xu, Bin ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/app15052769Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/15/5/2769/pdf?version=1741096546Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/15/5/2769/pdf?version=1741096546Direct OA link when available
- Concepts
-
Computer science, Computer vision, Artificial intelligence, Robotic arm, Separation (statistics), Frame (networking), Eye tracking, Telecommunications, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 73, 117, 194 |
| abstract_inverted_index.(from | 183 |
| abstract_inverted_index.146.3 | 129 |
| abstract_inverted_index.VGG16 | 115 |
| abstract_inverted_index.cause | 26 |
| abstract_inverted_index.frame | 110 |
| abstract_inverted_index.image | 18, 30 |
| abstract_inverted_index.model | 107, 122 |
| abstract_inverted_index.paper | 65 |
| abstract_inverted_index.robot | 71 |
| abstract_inverted_index.speed | 233 |
| abstract_inverted_index.study | 103 |
| abstract_inverted_index.these | 62 |
| abstract_inverted_index.(0.639 | 213 |
| abstract_inverted_index.(135.5 | 126 |
| abstract_inverted_index.0.94). | 186 |
| abstract_inverted_index.Kalman | 195 |
| abstract_inverted_index.RFBNet | 106 |
| abstract_inverted_index.camera | 11 |
| abstract_inverted_index.limits | 50 |
| abstract_inverted_index.models | 49 |
| abstract_inverted_index.needs. | 248 |
| abstract_inverted_index.reduce | 139 |
| abstract_inverted_index.robots | 8 |
| abstract_inverted_index.safety | 245 |
| abstract_inverted_index.subway | 1 |
| abstract_inverted_index.system | 72 |
| abstract_inverted_index.tunnel | 2, 69, 243 |
| abstract_inverted_index.vision | 85 |
| abstract_inverted_index.visual | 39 |
| abstract_inverted_index.0.629). | 215 |
| abstract_inverted_index.address | 61 |
| abstract_inverted_index.against | 202 |
| abstract_inverted_index.average | 178 |
| abstract_inverted_index.channel | 132 |
| abstract_inverted_index.control | 199 |
| abstract_inverted_index.current | 4 |
| abstract_inverted_index.decline | 28 |
| abstract_inverted_index.devices | 236 |
| abstract_inverted_index.employs | 104 |
| abstract_inverted_index.ensures | 200 |
| abstract_inverted_index.further | 154 |
| abstract_inverted_index.initial | 109 |
| abstract_inverted_index.method. | 83 |
| abstract_inverted_index.minimum | 164 |
| abstract_inverted_index.mounted | 12 |
| abstract_inverted_index.problem | 172 |
| abstract_inverted_index.reduces | 121 |
| abstract_inverted_index.replace | 150 |
| abstract_inverted_index.results | 217 |
| abstract_inverted_index.success | 209 |
| abstract_inverted_index.system. | 41 |
| abstract_inverted_index.through | 131, 159 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.accuracy | 36, 225 |
| abstract_inverted_index.accurate | 98 |
| abstract_inverted_index.approach | 221 |
| abstract_inverted_index.backbone | 116 |
| abstract_inverted_index.bounding | 156 |
| abstract_inverted_index.combined | 193 |
| abstract_inverted_index.devices, | 55 |
| abstract_inverted_index.drilling | 7, 15, 70, 94 |
| abstract_inverted_index.dual-arm | 68 |
| abstract_inverted_index.ensuring | 97 |
| abstract_inverted_index.follower | 81 |
| abstract_inverted_index.function | 146 |
| abstract_inverted_index.gradient | 170 |
| abstract_inverted_index.improves | 177, 223 |
| abstract_inverted_index.original | 114 |
| abstract_inverted_index.position | 90 |
| abstract_inverted_index.proposes | 66 |
| abstract_inverted_index.provides | 88 |
| abstract_inverted_index.quality, | 31 |
| abstract_inverted_index.resolves | 168 |
| abstract_inverted_index.solution | 241 |
| abstract_inverted_index.tracking | 82, 224 |
| abstract_inverted_index.achieving | 229 |
| abstract_inverted_index.affecting | 32 |
| abstract_inverted_index.algorithm | 192 |
| abstract_inverted_index.depthwise | 135 |
| abstract_inverted_index.detection | 48 |
| abstract_inverted_index.efficient | 100 |
| abstract_inverted_index.enclosing | 165 |
| abstract_inverted_index.filtering | 196 |
| abstract_inverted_index.inference | 232 |
| abstract_inverted_index.nonlinear | 205 |
| abstract_inverted_index.operation | 25 |
| abstract_inverted_index.precision | 179 |
| abstract_inverted_index.providing | 238 |
| abstract_inverted_index.real-time | 89 |
| abstract_inverted_index.replacing | 112 |
| abstract_inverted_index.separable | 136 |
| abstract_inverted_index.splitting | 133 |
| abstract_inverted_index.stability | 34 |
| abstract_inverted_index.tracking, | 189 |
| abstract_inverted_index.utilizing | 76 |
| abstract_inverted_index.vanishing | 171 |
| abstract_inverted_index.ShuffleNet | 118 |
| abstract_inverted_index.complexity | 45 |
| abstract_inverted_index.continuous | 188 |
| abstract_inverted_index.deployable | 240 |
| abstract_inverted_index.deployment | 51 |
| abstract_inverted_index.efficiency | 247 |
| abstract_inverted_index.hand–eye | 5, 74 |
| abstract_inverted_index.increasing | 207 |
| abstract_inverted_index.industrial | 58 |
| abstract_inverted_index.integrated | 6 |
| abstract_inverted_index.introduced | 148 |
| abstract_inverted_index.occlusions | 203 |
| abstract_inverted_index.operation. | 101 |
| abstract_inverted_index.optimizing | 155 |
| abstract_inverted_index.parameters | 123 |
| abstract_inverted_index.regression | 158 |
| abstract_inverted_index.robustness | 201 |
| abstract_inverted_index.stability, | 228 |
| abstract_inverted_index.(“eye”) | 87 |
| abstract_inverted_index.calculation | 161 |
| abstract_inverted_index.challenges, | 63 |
| abstract_inverted_index.complexity. | 141 |
| abstract_inverted_index.first-frame | 78 |
| abstract_inverted_index.operational | 227 |
| abstract_inverted_index.recognition | 40 |
| abstract_inverted_index.separation, | 75 |
| abstract_inverted_index.traditional | 152, 174 |
| abstract_inverted_index.Experimental | 216 |
| abstract_inverted_index.acquisition. | 19 |
| abstract_inverted_index.controllers. | 59 |
| abstract_inverted_index.convolutions | 137 |
| abstract_inverted_index.interference | 22 |
| abstract_inverted_index.localization | 79 |
| abstract_inverted_index.(“hand”), | 96 |
| abstract_inverted_index.Additionally, | 42, 142 |
| abstract_inverted_index.SiamRPN-based | 191 |
| abstract_inverted_index.computational | 44, 140 |
| abstract_inverted_index.construction, | 3 |
| abstract_inverted_index.disturbances, | 206 |
| abstract_inverted_index.localization, | 111 |
| abstract_inverted_index.long-distance | 24 |
| abstract_inverted_index.significantly | 222 |
| abstract_inverted_index.high-precision | 47 |
| abstract_inverted_index.construction’s | 244 |
| abstract_inverted_index.resource-constrained | 53 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.05537938 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |