Vision-Guided Collaborative Robotics with Adaptive Control for Intelligent Automation Systems: A Case Study

Abstract

The fast-paced move to Industry 4.0 has promoted the use of collaborative robots (cobots) that can work safely with human operators but can quickly respond to changing production needs. Nevertheless, most current robotic systems are not flexible and environmentally conscious to deal with product variants, unstable assembly environments, and unstructured worlds. In this case study, the design, integration and deployment of vision-guided collaborative robotic system with adaptive control to have intelligent automation was described. The offered system combines a stereo vision module with high resolution and real-time capability to detect objects and estimate their pose, as well as an adaptive control algorithm pertaining constant adjustments of trajectories to ensure that part position and orientation vary. Within an industrial automation cell used in the assembly of electronic components, the system achieved dramatic performance benefits, receiving a 32 percent reduction in task cycle time, 25 percent improvement in positional accuracy, and a marked improvement in setup time when changing the product being assembled. The findings suggest the opportunities in integrating vision-based perception and adaptive control of cobots to create greater efficiencies during operations, increased flexibilities and cooperation between humans and robots in contemporary manufacturing facilities.