Machine vision can pose some challenges for robotics, as it is a complex and demanding technology that requires careful design, implementation, and maintenance. It can be expensive and sophisticated, requiring hardware and software components such as cameras, sensors, processors, and algorithms. Furthermore, machine vision can require specialized skills and knowledge to develop, integrate, and operate. Additionally, it can face high variability and uncertainty in the images captured by the cameras due to factors such as lighting, noise, distortion, occlusion, and motion. To deal with these issues advanced techniques may be necessary such as calibration, normalization, filtering, and error correction. Moreover, machine vision can have high ethical and social implications for robotics such as privacy, security, accountability, and trust issues. For example, it can enable robots to collect personal and sensitive data from the images which can pose risks for the privacy and security of the individuals involved. In addition to this it can enable robots to make decisions based on the images which can raise questions about the accountability and trust of the robots.