Research on Optimal Design of Robotic Arm Joint Angle Path Based on Optimization Algorithms Article Swipe
Cheng‐Di Dong
,
Shurui Fei
,
Han Gao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.54254/2755-2721/2025.26214
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.54254/2755-2721/2025.26214
This paper takes a six-degree-of-freedom robotic arm as the research object and proposes a unified framework for "joint angle path optimization": First, a zero-position precise kinematic model is established using D-H parameters; then, four typical working conditions are sequentially solvedunder only precision constraints, the Whale Optimization Algorithm (WOA) yields joint angles [97.34, 103.88, 13.55, 21.68, 94.19, 89.49] in a single search, with an end-effector error of 2.510 m; when introducing an energy consumption model and targeting both error and energy consumption, the WOA-Monte Carlo hybrid algorithm provides [43.56, 57.16, 38.46, 67.52, 90, 0], with an error of 198 mm and energy consumption of 98.1 J; in obstacle avoidance scenarios, an A* algorithm first plans a collision-free base path, and then the same optimizer is called at each station, resulting in [44.73, 44.72, 38.53, 67.19, 89.62, 0.23], with performance comparable to previous results; for multi-objective grasping tasks, after A* generates a traversal path, each target is independently optimized, achieving a cumulative error of 510 mm and total energy consumption of 119 J. Experiments demonstrate that the integrated method of "D-H kinematics + A* base planning + WOA-MC joint optimization" significantly outperforms baseline approaches in terms of precision, energy consumption, obstacle avoidance, and multi-objective scenarios, showcasing strong potential for engineering applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54254/2755-2721/2025.26214
- https://www.ewadirect.com/proceedings/ace/article/view/26214/pdf
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413611681
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413611681Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.54254/2755-2721/2025.26214Digital Object Identifier
- Title
-
Research on Optimal Design of Robotic Arm Joint Angle Path Based on Optimization AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-26Full publication date if available
- Authors
-
Cheng‐Di Dong, Shurui Fei, Han GaoList of authors in order
- Landing page
-
https://doi.org/10.54254/2755-2721/2025.26214Publisher landing page
- PDF URL
-
https://www.ewadirect.com/proceedings/ace/article/view/26214/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.ewadirect.com/proceedings/ace/article/view/26214/pdfDirect OA link when available
- Concepts
-
Robotic arm, Joint (building), Path (computing), Computer science, Algorithm, Artificial intelligence, Engineering, Structural engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.First, | 21 |
| abstract_inverted_index.WOA-MC | 185 |
| abstract_inverted_index.angles | 50 |
| abstract_inverted_index.called | 124 |
| abstract_inverted_index.energy | 71, 79, 100, 166, 196 |
| abstract_inverted_index.hybrid | 84 |
| abstract_inverted_index.method | 176 |
| abstract_inverted_index.object | 10 |
| abstract_inverted_index.single | 59 |
| abstract_inverted_index.strong | 204 |
| abstract_inverted_index.target | 153 |
| abstract_inverted_index.tasks, | 145 |
| abstract_inverted_index.yields | 48 |
| abstract_inverted_index.103.88, | 52 |
| abstract_inverted_index.[43.56, | 87 |
| abstract_inverted_index.[44.73, | 130 |
| abstract_inverted_index.[97.34, | 51 |
| abstract_inverted_index.precise | 24 |
| abstract_inverted_index.robotic | 5 |
| abstract_inverted_index.search, | 60 |
| abstract_inverted_index.typical | 34 |
| abstract_inverted_index.unified | 14 |
| abstract_inverted_index.working | 35 |
| abstract_inverted_index.baseline | 190 |
| abstract_inverted_index.grasping | 144 |
| abstract_inverted_index.obstacle | 106, 198 |
| abstract_inverted_index.planning | 183 |
| abstract_inverted_index.previous | 140 |
| abstract_inverted_index.proposes | 12 |
| abstract_inverted_index.provides | 86 |
| abstract_inverted_index.research | 9 |
| abstract_inverted_index.results; | 141 |
| abstract_inverted_index.station, | 127 |
| abstract_inverted_index.Algorithm | 46 |
| abstract_inverted_index.WOA-Monte | 82 |
| abstract_inverted_index.achieving | 157 |
| abstract_inverted_index.algorithm | 85, 111 |
| abstract_inverted_index.avoidance | 107 |
| abstract_inverted_index.framework | 15 |
| abstract_inverted_index.generates | 148 |
| abstract_inverted_index.kinematic | 25 |
| abstract_inverted_index.optimizer | 122 |
| abstract_inverted_index.potential | 205 |
| abstract_inverted_index.precision | 41 |
| abstract_inverted_index.resulting | 128 |
| abstract_inverted_index.targeting | 75 |
| abstract_inverted_index.traversal | 150 |
| abstract_inverted_index.approaches | 191 |
| abstract_inverted_index.avoidance, | 199 |
| abstract_inverted_index.comparable | 138 |
| abstract_inverted_index.conditions | 36 |
| abstract_inverted_index.cumulative | 159 |
| abstract_inverted_index.integrated | 175 |
| abstract_inverted_index.kinematics | 179 |
| abstract_inverted_index.optimized, | 156 |
| abstract_inverted_index.precision, | 195 |
| abstract_inverted_index.scenarios, | 108, 202 |
| abstract_inverted_index.showcasing | 203 |
| abstract_inverted_index.Experiments | 171 |
| abstract_inverted_index.consumption | 72, 101, 167 |
| abstract_inverted_index.demonstrate | 172 |
| abstract_inverted_index.engineering | 207 |
| abstract_inverted_index.established | 28 |
| abstract_inverted_index.introducing | 69 |
| abstract_inverted_index.outperforms | 189 |
| abstract_inverted_index.parameters; | 31 |
| abstract_inverted_index.performance | 137 |
| abstract_inverted_index.solvedunder | 39 |
| abstract_inverted_index.Optimization | 45 |
| abstract_inverted_index.constraints, | 42 |
| abstract_inverted_index.consumption, | 80, 197 |
| abstract_inverted_index.end-effector | 63 |
| abstract_inverted_index.sequentially | 38 |
| abstract_inverted_index.applications. | 208 |
| abstract_inverted_index.independently | 155 |
| abstract_inverted_index.optimization" | 187 |
| abstract_inverted_index.significantly | 188 |
| abstract_inverted_index.zero-position | 23 |
| abstract_inverted_index.collision-free | 115 |
| abstract_inverted_index.optimization": | 20 |
| abstract_inverted_index.multi-objective | 143, 201 |
| abstract_inverted_index.six-degree-of-freedom | 4 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.39164145 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |