By Maia Jenkins

Amazon is testing out a new class of AI-powered, self-navigating robots in their fulfillment centers. Unlike their earlier counterparts, these next-generation robots will be able to roam freely, helping associates with tasks such as transporting oversized and bulky items around harder-to-navigate parts of the facility floors. 

The e-commerce leader uses around 500,000 robots in its fulfillment centers, but their current robots are restricted in their movements, sticking to a grid of markers enabled by cloud-based algorithms. 

What sets the new robots apart, Amazon says, are their capacity to not only move without restriction, but their “semantic  understanding” – that is, the ability to make sense of the three-dimensional structure of their environment and distinguish the behaviors of objects within it. Using LIDAR (light detection and ranging) and computer vision technology, the robots collect data, map out their surroundings, and can learn to avoid both fixed and moving obstacles. Through this, the robots can make sense of ever-changing, crowded settings such as fulfillment centers. 

For now, Amazon has rolled out these newer robots in select FCs and assigned them a limited number of tasks. The goal is to integrate the robots safely and seamlessly into the everyday operations of Amazon fulfillment center associates, the company said.

A sectional view of the CAD model of the finger (top) and the prototype antagonistic variable stiffness finger mechanism (bottom).

By Amelia Podder

For decades researchers have worked to design robotic hands that mimic the dexterity of human hands in the ways they grasp and manipulate objects. However, these earlier robotic hands have not been able to withstand the physical impacts that can occur in unstructured environments. A research team has now developed a compact robotic finger for dexterous hands, while also being capable of withstanding physical impacts in its working environment.

The team of researchers from Harbin University of Technology (China) published their work in the journal Frontiers of Mechanical Engineering on October 14, 2022.

Robots often work in environments that are unpredictable and sometimes unsafe. Physical collisions cannot be avoided when multi-fingered robotic hands are required to work in unstructured environments, such as settings where obstacles move quickly or the robot is required to interact with humans or other robots. 

The energy generated by these impacts can damage the hardware systems of the robotic hands. The current dexterous hands are rigid and therefore can be easily damaged by physical impacts and collisions. There is a need for robots equipped with sturdy, dexterous hands that can withstand physical impacts. The research team worked to create a robotic finger that could mimic the human finger in dexterity and also in its ability to absorb and withstand physical impacts.

Soft gripper grasps succulent.

If you’ve ever played the claw game at an arcade, you know how hard it is to grab and hold onto objects using robotics grippers. Imagine how much more nerve-wracking that game would be if, instead of plush stuffed animals, you were trying to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship. 

Most of today’s robotic grippers rely on embedded sensors, complex feedback loops, or advanced machine learning algorithms, combined with the skill of the operator, to grasp fragile or irregularly shaped objects. But researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated an easier way.

Taking inspiration from nature, they designed a new type of soft, robotic gripper that uses a collection of thin tentacles to entangle and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles, or filaments, are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap around objects and doesn’t require sensing, planning, or feedback control. 

The breakthrough is expected to help improve the efficiency of regenerative medicine, such as stem cell delivery.

Scientists have developed a mass-production method for biodegradable microrobots that can dissolve into the body after delivering cells and medications.

In order to create a technology that can produce more than 100 microrobots per minute that can be disintegrated in the body, Professor Hongsoo Choi’s team at the Department of Robotics and Mechatronics Engineering at the Daegu Gyeongbuk Institute of Science & Technology (DGIST)worked with Professor Sung-Won Kim’s team at Seoul St. Mary’s Hospital, Catholic University of Korea, and Professor Bradley J. Nelson’s team at ETH Zurich.

There are many approaches to building microrobots with the goal of minimally invasive targeted precision treatment. The most popular of them is the ultra-fine 3D printing process known as the two-photon polymerization method, which triggers polymerization in synthetic resin by intersecting two lasers.

This technique has the ability to create structures with nanometer-level accuracy. The drawback is that it takes a lot of time to create a single microrobot since voxels, the pixels realized by 3D printing, must be successively cured. In addition, during the two-photon polymerization process, the magnetic nanoparticles in the robot may block the light path. When utilizing highly concentrated magnetic nanoparticles, the process result may not be uniform.