Understanding how machine vision works will help you see if machine vision will clear up specific application difficulties in manufacturing or processing.
MIT researchers have developed a system that allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable, which could be useful in plant disaster and safety situations.
The aerospace industry is a tough, exacting industry. Robot users and integrators face challenges such as increased human-robot collaboration and the constant demand for precision and efficiency.
MIT researchers have developed a semantic parser that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend computing’s capabilities.
Collaborative robots will continue to evolve and future research may allow them to become more interactive and adaptable to human behavior.
Robotiq's 2F-85 and 2F-140 adaptive grippers are designed for collaborative robots and their finger bases have been redesigned to simplify fingertip changeover and help ensure a strong grip.
Fenceless robots are taking on a number of different sizes and forms beyond the collaborative robot as robotic technology has progressed.
Integrators have developed sophisticated robotic machine vision systems comprising numerous cameras mounted on a fixed frame to capture many images of an automotive product for quality assurance purposes.
Vision-guided robots, from industrial robotic arms to autonomous mobile robots (AMRs), are a quickly growing segment of robotics thanks to trends like 3-D vision and increased adoption.
MIT researchers have now devised a way to help robots navigate environments more like humans do by letting robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they’ve learned before in similar situations.