As automation technology becomes more sophisticated and advanced, it is increasingly being used to automate off-highway applications. Automated guided vehicles (AGVs) and robots are becoming popular tools for this purpose, as they can provide a cost-effective, efficient and safe way to perform various tasks, such as material handling, inspection and maintenance. AGVs are self-navigating vehicles that can transport materials and products along predefined routes. They are typically equipped with sensors and cameras, which enable them to detect obstacles and follow pre-determined paths. AGVs often employ an onboard controller that allows them to make decisions and adjust their route if needed. Robots are more advanced than AGVs and are capable of performing more complex tasks such as welding, painting and assembly. They are usually equipped with application-specific sensors and machine vision cameras that allow them to identify objects, determine the best path to take, and detect any changes in the environment. Robotic systems are also able to adapt to changes in their environment through machine learning models, making them ideal for off-highway applications. Off-highway automation is becoming increasingly popular in the manufacturing and logistics industries, as companies look to increase efficiency and reduce costs while keeping safety at the forefront of their operations. AGVs and robots can provide this advantage, making them a valuable tool to make use of. In this article from MIT Technology Review, the use of drones and robots along with machine learning methods to perform industrial inspection is discussed. Intellimech’s experience with engineering off-highway automated systems, mobile control, and machine vision can help our partners’ projects go the distance. Connect with us today to discuss the specifics of your application.
Becoming an Expert at (Robotic) Manipulation
A common challenge when examining manufacturing or laboratory processes is successfully manipulating an object with a variable presentation, i.e. a different orientation, dimension, color, texture, etc., when an element of that process requires uniformity. For instance, consider a pick-and-place system that’s packing ceramic tiles. If the tiles (or any process with a comparatively “large” batch size) are exactly identical, the only variability in presentation may be their orientation on a conveyor. A simple mechanical feature could be used to align the tiles uniformly before presentation to whatever is being used to perform the pick-and-place, whether a SCARA robot, a traditional robot, or a cartesian system. If the tiles (or any process with a “small” or individual batch size) aren’t exactly identical, a machine vision system could determine the color, position offset, and rotation, then communicate that to a motion system which would compensate for the orientation during the pick to place the tile neatly with its same color. Along the same lines, the method used to mechanically accomplish the pick would need to accommodate the product variability; this could be done using anything from vacuum to force-monitored grippers depending on the target. In this article from MIT Technology Review, a research project to develop a more dexterous robot using machine learning is investigated. No matter the challenge with your process, Intellimech’s engineers have the knowledge and experience to help realize your goals and minimize risk. Connect with our team today to explore your particular objectives and how we can help create the application solution you’re seeking.
Pushing the Limits of Automation… In a Warehouse
When it comes to the successful deployment of a robotic system for your application, less variation in the process usually translates to a substantially higher probability for success. This especially holds true when machine vision systems are employed. Vision systems often require controlled lighting and lensing with repeatable target presentation to effectively perform the task they’ve been selected for, such as measuring, counting, or inspecting. End effectors, or end-of-arm robotic tooling, may often rely on a repeatable presentation of their subject in order to correctly manipulate it. In this article from MIT Technology Review, a contest sponsored by Amazon is profiled that seeks to push the boundaries of machine vision and robotic manipulators in order to better function in their fulfillment centers. The engineers at Intellimech are highly skilled at engineering and integrating the latest vision systems and customized end-of-arm tooling for your specific application to help you capture success.