  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.
