Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910795304009728 |
|---|---|
| author | Astar, Ahmet Nurcan, Ozan Demirel, Erk Ozen, Emir Kutlar, Ozan Stroppa, Fabio |
| author_facet | Astar, Ahmet Nurcan, Ozan Demirel, Erk Ozen, Emir Kutlar, Ozan Stroppa, Fabio |
| contents | Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing robots; specifically, the three-dimensional extension of the optimizer designed for planar manipulators. This tool is intended to be used by engineers and robot enthusiasts before manufacturing their robot: it suggests the optimal size of the robot for solving a specific task. The design process models a multi-objective optimization problem to refine a soft manipulator's kinematic chain. Thanks to the novel Rank Partitioning algorithm integrated into Evolutionary Computation (EC) algorithms, this method achieves high precision in reaching targets and is efficient in resource usage. Results show significantly high performance in solving three-dimensional tasks, whereas comparative experiments indicate that the optimizer features robust output when tested with different EC algorithms, particularly genetic algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments Astar, Ahmet Nurcan, Ozan Demirel, Erk Ozen, Emir Kutlar, Ozan Stroppa, Fabio Robotics Artificial Intelligence Neural and Evolutionary Computing Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing robots; specifically, the three-dimensional extension of the optimizer designed for planar manipulators. This tool is intended to be used by engineers and robot enthusiasts before manufacturing their robot: it suggests the optimal size of the robot for solving a specific task. The design process models a multi-objective optimization problem to refine a soft manipulator's kinematic chain. Thanks to the novel Rank Partitioning algorithm integrated into Evolutionary Computation (EC) algorithms, this method achieves high precision in reaching targets and is efficient in resource usage. Results show significantly high performance in solving three-dimensional tasks, whereas comparative experiments indicate that the optimizer features robust output when tested with different EC algorithms, particularly genetic algorithms. |
| title | Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments |
| topic | Robotics Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2501.00368 |