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.