Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithm Article Swipe
Ravikiran Pasumarthi
,
S. M. Bhagya P. Samarakoon
,
Mohan Rajesh Elara
,
B.J. Sheu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-84637-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-84637-0
Reconfigurable modular robots can be used in application domains such as exploration, logistics, and outer space. The robots should be able to assemble and work as a single entity to perform a task that requires high throughput. Selecting an optimum assembly position with minimum distance traveled by robots in an obstacle surrounding the environment is challenging. Therefore, this paper proposes a novel approach for optimizing the assembly zone of modular robots in heterogeneous obstacle environments. The method uses a multi-objective Genetic Algorithm (GA) to minimize total travel distance and individual distance disparities. Utilizing the A* algorithm for path planning ensures efficient navigation. A generic kinematic model enabling holonomic locomotion with any reconfiguration and a new modular robot design are also introduced. Hardware experiments have been conducted to validate the kinematic model’s applicability for holonomic navigation across different robot configurations. Simulations and physical experiments demonstrated the effectiveness of the proposed method in determining assembly zones, with GA outperforming multi-objective pattern search and random selection in terms of total distance and individual distances traveled by the robots.
Related Topics
Concepts
Modular design
Robot
Self-reconfiguring modular robot
Computer science
Holonomic
Genetic algorithm
Control reconfiguration
Kinematics
Obstacle
Motion planning
Task (project management)
Path (computing)
Simulation
Algorithm
Distributed computing
Artificial intelligence
Mobile robot
Robot control
Embedded system
Engineering
Machine learning
Law
Political science
Systems engineering
Operating system
Classical mechanics
Programming language
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-84637-0
- https://www.nature.com/articles/s41598-024-84637-0.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406040060
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406040060Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-84637-0Digital Object Identifier
- Title
-
Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-03Full publication date if available
- Authors
-
Ravikiran Pasumarthi, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara, B.J. SheuList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-84637-0Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-84637-0.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-024-84637-0.pdfDirect OA link when available
- Concepts
-
Modular design, Robot, Self-reconfiguring modular robot, Computer science, Holonomic, Genetic algorithm, Control reconfiguration, Kinematics, Obstacle, Motion planning, Task (project management), Path (computing), Simulation, Algorithm, Distributed computing, Artificial intelligence, Mobile robot, Robot control, Embedded system, Engineering, Machine learning, Law, Political science, Systems engineering, Operating system, Classical mechanics, Programming language, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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