Robot arm trajectory generation: Obstacle avoidance with computer vision

Exploiting the benefits of state-of-art object detection, we identify and localize the obstacles in the workspace of a robot arm. Given the obstacles’ position, we model the obstacles as ellipsoids. Based on the location, size and shape of the ellipsoids, we modulate a linear dynamical system for generating the robot trajectories that avoid the obstacles when moving towards a target. Whilst the passive controller of the robot arm facilitates a safe interaction with humans, the generation of the robot trajectories from dynamical system enables a rapid reconfiguration of the trajectories after perturbations.


Researcher:
Iason Batzianoulis


Related code:
- Object detection: https://github.com/yias/eurekaRes/tre...
- Robot arm motion generator: https://github.com/yias/robot_arm_motion
- Kuka LWR control interface: https://github.com/epfl-lasa/kuka-lwr...
- Mask R-CNN: https://github.com/matterport/Mask_RCNN



Related work:
- Mask R-CNN, https://arxiv.org/abs/1703.06870
- A Dynamical System Approach to Realtime Obstacle Avoidance, https://infoscience.epfl.ch/record/17...
- Passive Interaction Control with Dynamical Systems, https://infoscience.epfl.ch/record/22...



Acknowledgements:
This research is supported by the Swiss National Science Foundation through the National Centre of Competence in Research Robotics and the Hasler Foundation.