Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles

Andreea Tulbure; Oussama Khatib

IROS 2020poster19 citations

Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles

Abstract

Robots have been successfully used in well-structured and deterministic environments, but they are still unable to function in unstructured environments mainly because of missing reliable real-time systems that integrate perception and control. In this paper, we close the loop between perception and control for real-time obstacle avoidance by introducing a new robust perception algorithm and a new collision avoidance strategy, which combines local artificial potential fields with global elastic planning to maintain the convergence towards the goal. We evaluate our new approach in real-world experiments using a Franka Panda robot and show that it is able to robustly avoid dynamic or even partially occluded obstacles while performing position or path following tasks.

BibTeX

@inproceedings{iros2020_closingtheloopre,
  title = {Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles},
  author = {Andreea Tulbure and Oussama Khatib},
  booktitle = {IROS 2020},
  year = {2020}
}