• DOI:10.48550/arXiv.2209.13628
• Corpus ID: 252568143

Intercepting A Flying Target While Avoiding Moving Obstacles: A Unified Control Framework With Deep Manifold Learning

@article{Dastider2022InterceptingAF,
  title={Intercepting A Flying Target While Avoiding Moving Obstacles: A Unified Control Framework With Deep Manifold Learning},
  author={Apan Dastider and Mingjie Lin},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.13628}
}

• Apan Dastider, Mingjie Lin
• Published 27 September 2022
• Computer Science
• ArXiv

—Real-time interception of a fast-moving object by a robotic arm in cluttered environments filled with static or dynamic obstacles permits only tens of milliseconds for reaction times, hence quite challenging and arduous for state-of-the-art robotic planning algorithms to perform multiple robotic skills, for instance, catching the dynamic object and avoiding obstacles, in parallel. This paper proposes an unified frame- work of robotic path planning through embedding the high-dimensional temporal information contained in the event stream to distinguish between safe and colliding trajectories into a low-dimension space manifested with a pre-constructed 2D densely connected graph. We then leverage a fast graph-traversing strategy to generate the motor commands necessary to ef- fectively avoid the approaching obstacles while simultaneously intercepting a fast-moving objects. The most distinctive feature of our methodology is to conduct both object interception and obstacle avoidance within the same algorithm framework based on deep manifold learning. By leveraging a highly efficient diffusion-map based variational autoencoding and Extended Kalman Filter(EKF), we demonstrate the effectiveness of our approach on an autonomous 7-DoF robotic arm using only onboard sensing and computation. Our robotic manipulator was capable of avoiding multiple obstacles of different sizes and shapes while successfully capturing a fast-moving soft ball thrown by hand at normal speed in different angles. video demonstrations of our experiments can be found in https://sites.google.com/view/