Articles
Dr Sarah Fletcher, head of the Industrial Psychology and Human Factors Group at Cranfield University
Robots and intelligent automation in manufacturing are becoming more sophisticated – this means people will be required to work more closely and more frequently with robots. As new technologies cannot be used if workforces do not accept them, we need to understand the ergonomic and psychological impacts of close human-robot working relationships to ensure that systems are designed and implemented successfully.
Currently, operators have been trained to be highly cautious of robots and are often segregated from them to prevent collision injury. However, they will now be expected to work more closely and collaboratively with them, which could induce anxiety and require cognitive and cultural adaptation. We do not yet know how decision-making and other cognitive processes might be affected.
What we do know is that a historical lack of consideration of human factors is a primary cause of technology adoption failures in industry. There is a real need to include human data, as well as technical data, in automation design concepts and implementation programmes.
A recent report by MEPs called for the adoption of a set of comprehensive rules for the safe and ethical design of artificial intelligence and robots. This is a positive initiative as there has been relatively little preparation for the inevitable rise of robots in everyday life. Overlooking these issues could potentially lead to performance impairments, lack of worker acceptance, and lack of technology adoption.
At the Industrial Psychology and Human Factors research group at Cranfield University we specialise in the development of the design and implementation of robotics and intelligent automation for collaboration with human operators. We are looking at ways we can shift the attitudes people have towards manufacturing robots. People may view them as limited or purely functional, and not safe to work around. For example, we have already developed the first psychometric measure of human trust in industrial robots. The trust measurement scale was developed in two phases. In phase one, a study was conducted to collect participants’ opinions qualitatively. This led to the identification of trust-related themes and a related pool of questionnaire items was generated. In the second phase, three human-robot trials were carried out in which the questionnaire items were applied to participants using three types of industrial robots. The results were statistically analysed to identify the key factors impacting trust and from these generate a trust measurement scale for industrial human-robot collaboration.
Robot designers are becoming more aware of these issues, but there is still lots more work to do, particularly as different organisations, regions, workforces, individual workers, robot features and tasks will all bring their own challenges.
Robotics and intelligent automation have many benefits to bring society but we need to be careful to ensure the rate of implementation doesn’t race too far ahead before we fully understand, and are prepared for, potential negative impacts.