Project Summary:
Automation is becoming increasingly common in manufacturing and assembly plants. Most applications are either fully automated or entirely manual. Robots excel at repetitive, tedious, and dangerous tasks, while humans handle complex, dynamic, and thought-intensive work. The future lies in hybrid workspaces where the strengths of humans and robots complement each other, with robots excelling in accuracy, repeatability, speed, and strength, and humans excelling in creativity, emotional intelligence, and complex decision-making.
Project Background:
The manufacturing sector has witnessed a steady increase in automation. However, most implementations have traditionally ranged from fully manual to fully automated. This is where collaborative robots (cobots) come into play, addressing tasks that necessitate both human and robotic skills. They serve as an upgrade for tasks that are highly repetitive and too complex for complete automation.
A hybrid workspace leverages a robot’s repeatability, precision, and speed alongside a human’s complexity, empathy, and flexibility. This approach, known as Human-Robot Collaboration (HRC), aims to create a shared environment where humans and robots work together. It bridges the gap between manual and fully automated production, involving human workers in direct interaction with machines—unlike fully automated setups where robots are typically separated from humans during operation.
HRC plays a pivotal role in the emerging phase of industrialization (Industry 5.0), emphasizing collaboration between humans and advanced robotics to enhance workplace processes while ensuring human involvement in decision-making.
This research holds significant potential across industries. By exploring how automation levels impact mental workload and trust, companies can optimize cobot integration to boost productivity, safety, and worker satisfaction. This study contributes not only to the academic field of Human-Computer Interaction (HCI) but also offers practical insights for industries seeking effective implementation of collaborative robotics.
In this study, a participant is positioned near a collaborative robot and is tasked with assembling a miniature lamppost using magnetic wooden blocks, dowels, and a post-cap.
The human and robot alternately place the cubes on an elevated base. Participants were divided into two groups:
- Automated Group: The participant shares control over the robot, which does not require activation after the participant’s turn.
- Semi-Automated Group: The participant has full control over the robot, which needs to be activated after each turn.
The participant and the robot collaborate to build an end product that resembles a miniature lamppost:
Measures as a part of the study:
- Assembly Time: The total time taken to complete the assembly task.
- Detection Response Time (DRT): A secondary task that assesses the reaction time to an auditory stimulus presented at random intervals during the study. The stimulus can be activated using a red switch/buzzer located in the workspace.
- Heart Rate Measures: Heart rate variability (HRV), Heart rate (HR), and the LF/HF Ratio are recorded using a chest-strapped heart rate monitor (Polar H7) to analyze the participant’s autonomic nervous system (ANS) response while working alongside the robot. These measures help to study the mental workload.
- Perceived Mental Workload: The NASA-TLX scale assessed the perceived mental workload during the experiment. It includes six subscales: Mental Demand, Physical Demand, Temporal Demand, Frustration, Effort, and Performance.
- Trust ratings: The Trust in Automation (TiA) Scale, consisting of six subscales, was used to evaluate perceived trust: Reliability/Competence, Understanding/Predictability, Familiarity, Intention of Developers, Propensity to Trust, and Trust in Automation.