Intelligent Robotics Safeguarding

Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Recent advances in technology allow machine safeguarding to shift from a system that completely shuts down the hazardous part of a machine, regardless of the action, to one with a controlled response. This intelligent robotics safeguarding can be based on conditions such as the type of task, how it is performed, entry and exit locations, and the operator’s movement within the hazard zone. Such a strategy could increase production rates by allowing robots to operate at higher speeds within dynamic environments.
When used as part of a preventative maintenance program, reliability data can predict component failure rates and reduce the probability that operators will access the hazard zone. Programming techniques, such as function blocks to monitor component usage, can be used to evaluate trends.
SQL (Structured Query Language) databases can track access and frequency trends, which can lead to design improvements and indicate changes affecting the system. Advanced 3-D safety sensing devices can provide additional flexibility for using zones and tracking intrusion without creating barriers that interfere with operator movement or increase the likelihood of bypassing safeguards. They can also be used with manual load and unload stations to monitor how frequently safeguarding conditions change and further improve design ergonomics.
Robots can use safe motion to reduce speed and monitor position or control access through zone sets instead of forcing complete shutdowns. The inherently safe design of the power and force limited robots used in collaborative applications allow for more interaction and mobility and, thus, enables operators to quickly adapt to changing needs.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0293
Pages
7
Citation
Hull, T., "Intelligent Robotics Safeguarding," SAE Int. J. Engines 10(2):215-221, 2017, https://doi.org/10.4271/2017-01-0293.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0293
Content Type
Journal Article
Language
English