Creating an Obstacle Memory Through Event-Based Stereo Vision and Robotic Proprioception

Resource type
Conference
Author(s)
Lea Steffen, Benedict Hauck, Jacques Kaiser, Jakob Weinland, Stefan Ulbrich, Daniel Reichard, Arne Roennau, Rüdiger Dillmann
Journal
IEEE 15th International Conference on Automation Science and Engineering (CASE)
Year
2019
Month
August
Book title
IEEE 15th International Conference on Automation Science and Engineering (CASE)
Abstract
To guarantee safety in a shared work space between humans and robots, flexible robotic motion control is required. Unfortunately, path planning algorithms for complex robotic systems are too computationally expensive to enable a real-time solution on conventional hardware. With the long-term goal of performing a reactive path planning algorithm, we apply neuromorphic sensors and Spiking Neural Networks to create an obstacle memory of a robot’s work space. We create a neuron population representing all objects of the robot’s work cell except for the robot itself. Furthermore, we adapt the network to preserve older states while still reacting to new events, obtaining a correct obstacle memory at any given point in time. For this purpose, we control a kinematic chain, the robot arm. Hereby, we use two sensor networks for proprioception and exteroception.
Online Sources
https://ieeexplore.ieee.org/document/8843238
DOI
10.1109/COASE.2019.8843238
Research focus
Service Robotics and Mobile Manipulation
Project
NeuroReact - Echtzeitfähige neuronale Planung für reaktive Industrieroboter
Download .bib
Download .bib
Published by
Lea Steffen