Robots Can Now Locate Your Hidden Remote Hidden Behind a Pile of Garments
Robots are becoming helpful for recovering missing items.
When a busy commuter is about to leave the house, they discover they have lost their keys and have to dig through a mountain of belongings to retrieve them. They hope they could quickly identify which mound of clutter had the keys as they quickly sort through the mess. That is exactly what a robotic system developed by MIT researchers can achieve. The RFusion system consists of a robotic gripper that has a camera and radio frequency (RF) antenna connected. Even if the object is hidden behind a pile and is entirely out of sight, it may be located and retrieved by fusing visual input from the camera with signals from the antenna. The RFfusion prototype that the researchers created makes use of inexpensive, battery-free RFID tags that can be adhered to objects and reflect signals sent by antennas. RF signals can pass through most materials, including a pile of dirty clothes that could be blocking your view of the keys, therefore RFfusion can find a tagged object inside a pile. The robotic arm automatically moves the things that are on top of the object, locates them precisely, grasps them, and confirms that it has picked up the proper thing using machine learning. Since the robotic arm, AI, camera, and antenna are all fully integrated, RFfusion may operate in any setting without the need for a specific setup. The current prototype of RFusion isn’t quite fast enough for these uses yet, but it could have many more diverse applications in the future, such as helping an elderly person complete daily tasks in the home or sorting through piles to fulfill orders in a warehouse or an auto manufacturing facility.
This concept of being able to locate objects in a chaotic environment is an open challenge that we have been attempting to solve for several years. The necessity for robots that can see under a pile of objects is developing in the modern industrial world. In the short term, this could have a lot of applications in manufacturing and warehouse settings, according to senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and head of the Signal Kinetics group at the MIT Media Lab. “Right now, you can think of this as a Roomba on steroids,” he said.
RFfusion starts looking for an item using its antenna, which bounces signals off the RFID tag to identify a spherical region where the tag is placed. It narrows down the object’s position by fusing that sphere with the camera input. For instance, the object cannot be found on an empty section of a table. The robot would need to swing its arm extensively about the room and take more measurements after it has a basic notion of where the object is, which is slow and wasteful, to determine the exact position. The neural network that can optimize the robot’s route to the target was trained by the researchers using reinforcement learning. The algorithm is learned using a reward system and trial-and-error in reinforcement learning.
Small radio-frequency identification (RFID) method
The Massachusetts Institute of Technology (MIT) researchers that developed the prototype have a robotic arm with a camera and radio frequency antenna attached to its gripper. It operates by bouncing signals off a small radio-frequency identification (RFID) tag that may be affixed to a lost item to help find it using its antenna. Passports, library books, contactless cards, and the Oyster system, which is used by more than 10 million people to pay for public transportation in London, all already contain low-cost, battery-free RFID chips. They are also used by airlines to track luggage and by shops to deter stealing.
Since radio frequency (RF) signals can pass through most surfaces, even a pile of filthy clothing that could be blocking the keys, they are ideal in this situation. The antenna locates a spherical region in which the RFID tag is positioned once it has established communication with the tag, which it accomplishes by reflecting signals off it like how sunlight reflects off a mirror. After combining this sphere’s information with that from its camera, the robot may more precisely locate the object, relocate any objects that are on top of it, and finally grip the object after making sure it is the proper one.
The system’s speed will eventually be increased to enable seamless movement rather than sporadic measurement stops, according to the researchers. This would make it possible to employ RFusion in a busy warehouse or factory environment. Beyond its possible industrial applications, the technology may potentially be added to future smart houses to help people with a variety of household chores, according to Boroushaki.
Mobile robots
Mobile robots are doing activities that might burden employees with repetitive stress injuries or tiredness in warehouses, hospitals, and industries. Heavy loads are carried by AMRs (Autonomous Mobile Robots) in industries, meals, medications, and laundry are delivered by AMRs in hospitals, and AMRs choose and choose goods from warehouse shelves for shipping. AMRs can sometimes fill open positions without applications, while in other situations, they collaborate with people. Better mapping systems are always being included in AMRs to designate paths across the office, better sensors are being developed to safeguard employees who share space with AMRs, and the best ways to recharge AMRs are being sought. Here, leaders who are developing new robots consider what may happen in the future.
More Trending Stories
- Dogecoin is Entering the Red Territory! Is it the Best Time to Sell?
- AI Writes an Academic Paper About Itself and Researchers Try to Publish It!
- Following Roe, Digital Ad Companies Are Up for a Self-Location Data Limitation
- Will iPhone15 Come with a Metaverse Twist? Time will Answer
- Multi-Modal AI Is the New Frontier in Processing Big Data
- What Will Happen If Google’s LaMDA Becomes Biased?
- Top 10 Power BI interview questions aspirants should master in 2022
The post Robots Can Now Locate Your Hidden Remote Hidden Behind a Pile of Garments appeared first on .
Robots are becoming helpful for recovering missing items.
When a busy commuter is about to leave the house, they discover they have lost their keys and have to dig through a mountain of belongings to retrieve them. They hope they could quickly identify which mound of clutter had the keys as they quickly sort through the mess. That is exactly what a robotic system developed by MIT researchers can achieve. The RFusion system consists of a robotic gripper that has a camera and radio frequency (RF) antenna connected. Even if the object is hidden behind a pile and is entirely out of sight, it may be located and retrieved by fusing visual input from the camera with signals from the antenna. The RFfusion prototype that the researchers created makes use of inexpensive, battery-free RFID tags that can be adhered to objects and reflect signals sent by antennas. RF signals can pass through most materials, including a pile of dirty clothes that could be blocking your view of the keys, therefore RFfusion can find a tagged object inside a pile. The robotic arm automatically moves the things that are on top of the object, locates them precisely, grasps them, and confirms that it has picked up the proper thing using machine learning. Since the robotic arm, AI, camera, and antenna are all fully integrated, RFfusion may operate in any setting without the need for a specific setup. The current prototype of RFusion isn’t quite fast enough for these uses yet, but it could have many more diverse applications in the future, such as helping an elderly person complete daily tasks in the home or sorting through piles to fulfill orders in a warehouse or an auto manufacturing facility.
This concept of being able to locate objects in a chaotic environment is an open challenge that we have been attempting to solve for several years. The necessity for robots that can see under a pile of objects is developing in the modern industrial world. In the short term, this could have a lot of applications in manufacturing and warehouse settings, according to senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and head of the Signal Kinetics group at the MIT Media Lab. “Right now, you can think of this as a Roomba on steroids,” he said.
RFfusion starts looking for an item using its antenna, which bounces signals off the RFID tag to identify a spherical region where the tag is placed. It narrows down the object’s position by fusing that sphere with the camera input. For instance, the object cannot be found on an empty section of a table. The robot would need to swing its arm extensively about the room and take more measurements after it has a basic notion of where the object is, which is slow and wasteful, to determine the exact position. The neural network that can optimize the robot’s route to the target was trained by the researchers using reinforcement learning. The algorithm is learned using a reward system and trial-and-error in reinforcement learning.
Small radio-frequency identification (RFID) method
The Massachusetts Institute of Technology (MIT) researchers that developed the prototype have a robotic arm with a camera and radio frequency antenna attached to its gripper. It operates by bouncing signals off a small radio-frequency identification (RFID) tag that may be affixed to a lost item to help find it using its antenna. Passports, library books, contactless cards, and the Oyster system, which is used by more than 10 million people to pay for public transportation in London, all already contain low-cost, battery-free RFID chips. They are also used by airlines to track luggage and by shops to deter stealing.
Since radio frequency (RF) signals can pass through most surfaces, even a pile of filthy clothing that could be blocking the keys, they are ideal in this situation. The antenna locates a spherical region in which the RFID tag is positioned once it has established communication with the tag, which it accomplishes by reflecting signals off it like how sunlight reflects off a mirror. After combining this sphere’s information with that from its camera, the robot may more precisely locate the object, relocate any objects that are on top of it, and finally grip the object after making sure it is the proper one.
The system’s speed will eventually be increased to enable seamless movement rather than sporadic measurement stops, according to the researchers. This would make it possible to employ RFusion in a busy warehouse or factory environment. Beyond its possible industrial applications, the technology may potentially be added to future smart houses to help people with a variety of household chores, according to Boroushaki.
Mobile robots
Mobile robots are doing activities that might burden employees with repetitive stress injuries or tiredness in warehouses, hospitals, and industries. Heavy loads are carried by AMRs (Autonomous Mobile Robots) in industries, meals, medications, and laundry are delivered by AMRs in hospitals, and AMRs choose and choose goods from warehouse shelves for shipping. AMRs can sometimes fill open positions without applications, while in other situations, they collaborate with people. Better mapping systems are always being included in AMRs to designate paths across the office, better sensors are being developed to safeguard employees who share space with AMRs, and the best ways to recharge AMRs are being sought. Here, leaders who are developing new robots consider what may happen in the future.
More Trending Stories
- Dogecoin is Entering the Red Territory! Is it the Best Time to Sell?
- AI Writes an Academic Paper About Itself and Researchers Try to Publish It!
- Following Roe, Digital Ad Companies Are Up for a Self-Location Data Limitation
- Will iPhone15 Come with a Metaverse Twist? Time will Answer
- Multi-Modal AI Is the New Frontier in Processing Big Data
- What Will Happen If Google’s LaMDA Becomes Biased?
- Top 10 Power BI interview questions aspirants should master in 2022
The post Robots Can Now Locate Your Hidden Remote Hidden Behind a Pile of Garments appeared first on .
Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to publish your materials, please contact us by email – [email protected]. The content will be deleted within 24 hours.