Mitigating the effect of shadows in vision-based industrial robot

Modern manufacturing processes depend heavily on industrial robots with vision systems installed, which allow them to perform tasks like tracking, manipulation, and object recognition. But one major obstacle that affects the precision and dependability of these vision-based systems is the existence of shadows. In this paper, we specifically examine methods for reducing shadow effects in industrial robots using vision, with an emphasis on the KUKA robotic platform. Reduced contrast, distorted object shapes, and incorrect object classification are among the main issues that have been found. These issues can impair robot performance and lower productivity. Our work attempts to tackle these issues by taking a multifaceted approach that includes both software and hardware fixes. Our suggested method includes the Patmax algorithm for obect detection and correction, sophisticated image processing algorithms, and optimized lighting configurations. We validate our methods experimentally on the KUKA robotic platform and show that our approaches effectively reduce shadow effects. All things considered, our work strengthens the durability and dependability of vision-based industrial robots, which has important ramifications for raising productivity and performance in industrial automation applications.