Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multi-Fingered In-Hand Manipulation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911947481415680 |
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| author | Gao, Xiao Yao, Kunpeng Khadivar, Farshad Billard, Aude |
| author_facet | Gao, Xiao Yao, Kunpeng Khadivar, Farshad Billard, Aude |
| contents | Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must be generated in real-time, as the rapid changes in hand and finger positions necessitate instantaneous recalculations to prevent collisions and ensure undisturbed movement. This study introduces a real-time approach to motion planning in high-dimensional spaces. We first explicitly model the collision-free space using neural networks that are retrievable in real time. Then, we combined the C-space representation with closed-loop control via dynamical system and sampling-based planning approaches. This integration enhances the efficiency and feasibility of path-finding, enabling dynamic obstacle avoidance, thereby advancing the capabilities of multi-fingered robotic hands for in-hand manipulation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06955 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multi-Fingered In-Hand Manipulation Gao, Xiao Yao, Kunpeng Khadivar, Farshad Billard, Aude Robotics Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must be generated in real-time, as the rapid changes in hand and finger positions necessitate instantaneous recalculations to prevent collisions and ensure undisturbed movement. This study introduces a real-time approach to motion planning in high-dimensional spaces. We first explicitly model the collision-free space using neural networks that are retrievable in real time. Then, we combined the C-space representation with closed-loop control via dynamical system and sampling-based planning approaches. This integration enhances the efficiency and feasibility of path-finding, enabling dynamic obstacle avoidance, thereby advancing the capabilities of multi-fingered robotic hands for in-hand manipulation tasks. |
| title | Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multi-Fingered In-Hand Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.06955 |