In the growing field of warehouse automation, managing hundreds of robots zipping through a large warehouse is a logistical challenge. Delivery paths, potential collisions and congestion all pose significant issues, making the task a complex problem that even the best algorithms find hard to manage. To solve this, a team of MIT researchers has developed…
In order to improve efficiency in large-scale robotic warehouses, a team of researchers from the Massachusetts Institute of Technology (MIT) have developed a deep learning model which assists in navigating robots to decongest warehouse floors. The way this model works is by splitting the hundreds of robots into smaller, manageable groups which are easier for…
Researchers at MIT have developed an artificial intelligence tool to improve the efficiency of robotic warehouses. The researchers, drawing from their work in moderating traffic congestion, created a deep-learning model that can process information about the warehouse, including robot paths, tasks, and obstacles, to optimize warehouse functionality. By grouping the robots into smaller units, the…
In crowded robotic warehouses, managing hundreds of robots to navigate efficiently and avoid collisions is a complex challenge. This has prompted a group of MIT researchers to create a deep-learning model to tackle the problem. Using AI, the team has developed an innovative model that encapsulates details about the warehouse environment - the robots, planned…
As automated warehouses become increasingly popular in various industries, ensuring the efficiency and safety of hundreds of robots navigating such spaces is a significant challenge. Notably, even top-performing algorithms struggle to keep pace with the demands of e-commerce or manufacturing. To address this, a group of MIT researchers, who use AI to mitigate traffic congestion,…
Navigating hundreds of robots in a warehouse without causing accidents is a growing challenge for many industries, from e-commerce to automotive production. To address this issue, a team of researchers at the Massachusetts Institute of Technology (MIT) has developed an AI-based approach that increases efficiency and reduces congestion.
The team devised a deep learning model that…
MIT researchers have developed a technique to train robots on multiple tasks by combining and optimising data from a variety of sources. At the core of their work is a type of generative AI known as a 'diffusion model', which learns from a specific dataset to complete a task. However, the particular innovation here lies…
Scientists at MIT have been working on the design and control of a reconfigurable, squishy, soft robot, similar in nature to 'slime', that has potential applications in healthcare, wearable devices and industrial systems due to its ability to shape-shift to complete varying tasks. These soft robots currently only exist in labs and do not possess…