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.

翻译：机器人学中的灵巧手内操作，尤其是使用多指机械手时，由于需要精细避免手指与被操作物体之间的碰撞，带来了重大挑战。所有手指的无碰撞路径必须实时生成，因为手和手指位置的快速变化需要即时重新计算以防止碰撞并确保无干扰运动。本研究提出了一种在高维空间中进行实时运动规划的方法。我们首先使用可实时检索的神经网络显式建模无碰撞空间。然后，我们将该C空间表示与通过动态系统的闭环控制以及基于采样的规划方法相结合。这种集成提高了路径查找的效率和可行性，实现了动态避障，从而提升了多指机械手执行手内操作任务的能力。