Artificial Intelligence in Foreign Object Classification in Fenceless Robotic Work Cells Using 2-D Safety Cameras

Production systems using robotic manipulators have become common in the last few decades, and the trend is towards fenceless cells that save from space. Thus, the safety and flexibility of these systems have become more critical. The safety systems are based on either sensor data or camera images. Although the flexibility of the camera-based systems is better, conventional image processing methods are sensitive to the working environment. Artificial intelligence may be a powerful tool for them to adapt to change requirements quickly and improve accuracy and stability. In this study, a low-cost 2-D camera-based safety system was designed and installed in an experimental fenceless robotic work cell. The system controller was coupled with three alternative deep learning (ResNet-152, AlexNet, SqueezeNet) and three machine learning modules (support vector machine, random forest and decision tree). These modules were trained using photo images of ten distinct foreign objects penetrating the alarm zone. To include the ever-changing conditions of the industrial environment, disruptive effects including camera vibrations, shadows, reflections, illuminance variations etc. are included by using multiple images up to 550 for each class. Using the restricted data used for training and testing the six systems, the SqueezeNet deep learning model gave the best accuracy of 95% without any over-fitting. Despite this, machine learning-based models have been found to have 100 times faster prediction time than deep learning-based ones. Thus, the safety system can be adapted quickly to any possible changes and noise that may arise from working conditions is prevented, and time losses that may occur in industrial production may be prevented

Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera’s locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated

Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera’s locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated