For the more than 5 million people in the world who have undergone an upper-limb amputation, prosthetics have come a long way. Beyond traditional mannequin-like appendages, there is a growing number of commercial neuroprosthetics — highly articulated bionic limbs, engineered to sense a user’s residual muscle signals and robotically mimic their intended motions.
But this high-tech dexterity comes at a price. Neuroprosthetics can cost tens of thousands of dollars and are built around metal skeletons, with electrical motors that can be heavy and rigid. Full Article
Modern Material Handling
Robotics are a hot item in warehouses right now, with strong double-digit growth projected by most analysts over the next few years. There’s a big difference, however, between knowing that robotics has market momentum, and knowing how to properly scope a robotics solution.
The dilemma with scoping robotics for a DC is that plenty of “it depends” are engrained in finding a good fit. It depends on what workflow you want to automate It depends on order mix and volume, product dimensions, layout and labor considerations. That said, if you back up from these parameters, some best practices emerge around knowing operational priorities, having a handle on data like SKU dimensions, and being clear about the problem. Full Article
There are some tasks traditional robots — the rigid and metallic kind — simply aren’t cut out for. Soft-bodied robots, on the other hand, may be able to safely interact with people or slip into tight spaces with ease. For robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.
MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design. “The system not only learns a given task, but also how to best design the robot to solve that task,” said MIT student PhD student Alexander Amini. “Sensor placement is a very difficult problem to solve. So, having this solution is extremely exciting.” (Full Article)