Sci-Advent – Getting the right grip: Designing soft and sensitive robotic fingers

person holding black and silver hand tool
Photo by C Technical on

To develop a more human-like robotic gripper, it is necessary to provide sensing capabilities to the fingers. However, conventional sensors compromise the mechanical properties of soft robots. Now, scientists have designed a 3D printable soft robotic finger containing a built-in sensor with adjustable stiffness. Their work represents a big step toward safer and more dexterous robotic handling, which will extend the applications of robots to fields such as health and elderly care.

Although robotics has reshaped and even redefined many industrial sectors, there still exists a gap between machines and humans in fields such as health and elderly care. For robots to safely manipulate or interact with fragile objects and living organisms, new strategies to enhance their perception while making their parts softer are needed. In fact, building a safe and dexterous robotic gripper with human-like capabilities is currently one of the most important goals in robotics.

One of the main challenges in the design of soft robotic grippers is integrating traditional sensors onto the robot’s fingers. Ideally, a soft gripper should have what’s known as proprioception — a sense of its own movements and position — to be able to safely execute varied tasks. However, traditional sensors are rigid and compromise the mechanical characteristics of the soft parts. Moreover, existing soft grippers are usually designed with a single type of proprioceptive sensation; either pressure or finger curvature.

To overcome these limitations, scientists at Ritsumeikan University, Japan, have been working on novel soft gripper designs under the lead of Associate Professor Mengying Xie. In their latest study published in Nano Energy, they successfully used multimaterial 3D printing technology to fabricate soft robotic fingers with a built-in proprioception sensor. Their design strategy offers numerous advantages and represents a large step toward safer and more capable soft robots.

The soft finger has a reinforced inflation chamber that makes it bend in a highly controllable way according to the input air pressure. In addition, the stiffness of the finger is also tunable by creating a vacuum in a separate chamber. This was achieved through a mechanism called vacuum jamming, by which multiple stacked layers of a bendable material can be made rigid by sucking out the air between them. Both functions combined enable a three-finger robotic gripper to properly grasp and maintain hold of any object by ensuring the necessary force is applied.

Most notable, however, is that a single piezoelectric layer was included among the vacuum jamming layers as a sensor. The piezoelectric effect produces a voltage difference when the material is under pressure. The scientists leveraged this phenomenon as a sensing mechanism for the robotic finger, providing a simple way to sense both its curvature and initial stiffness (prior to vacuum adjustment). They further enhanced the finger’s sensitivity by including a microstructured layer among the jamming layers to improve the distribution of pressure on the piezoelectric material.

The use of multimaterial 3D printing, a simple and fast prototyping process, allowed the researchers to easily integrate the sensing and stiffness-tuning mechanisms into the design of the robotic finger itself. “Our work suggests a way of designing sensors that contribute not only as sensing elements for robotic applications, but also as active functional materials to provide better control of the whole system without compromising its dynamic behavior,” says Prof Xie. Another remarkable feature of their design is that the sensor is self-powered by the piezoelectric effect, meaning that it requires no energy supply — essential for low-power applications.

Overall, this exciting new study will help future researchers find new ways of improving how soft grippers interact with and sense the objects being manipulated. In turn, this will greatly expand the uses of robots, as Prof Xie indicates: “Self-powered built-in sensors will not only allow robots to safely interact with humans and their environment, but also eliminate the barriers to robotic applications that currently rely on powered sensors to monitor conditions.”

Let’s hope this technology is further developed so that our mechanical friends can soon join us in many more human activities!

Flexible self-powered multifunctional sensor for stiffness-tunable soft robotic gripper by multimaterial 3D printingNano Energy, 2021; 79: 105438 DOI: 10.1016/j.nanoen.2020.105438

Sci-Advent – ‘Electronic amoeba’ finds approximate solution to traveling salesman problem in linear time

Researchers at Hokkaido University and Amoeba Energy in Japan have, inspired by the efficient foraging behavior of a single-celled amoeba, developed an analog computer for finding a reliable and swift solution to the traveling salesman problem — a representative combinatorial optimization problem.

Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem. Scientific Reports, 2020; 10 (1) DOI: 10.1038/s41598-020-77617-7

Many real-world application tasks such as planning and scheduling in logistics and automation are mathematically formulated as combinatorial optimization problems. Conventional digital computers, including supercomputers, are inadequate to solve these complex problems in practically permissible time as the number of candidate solutions they need to evaluate increases exponentially with the problem size — also known as combinatorial explosion. Thus new computers called “Ising machines,” including “quantum annealers,” have been actively developed in recent years. These machines, however, require complicated pre-processing to convert each task to the form they can handle and have a risk of presenting illegal solutions that do not meet some constraints and requests, resulting in major obstacles to the practical applications.

These obstacles can be avoided using the newly developed “electronic amoeba,” an analog computer inspired by a single-celled amoeboid organism. The amoeba is known to maximize nutrient acquisition efficiently by deforming its body. It has shown to find an approximate solution to the traveling salesman problem (TSP), i.e., given a map of a certain number of cities, the problem is to find the shortest route for visiting each city exactly once and returning to the starting city. This finding inspired Professor Seiya Kasai at Hokkaido University to mimic the dynamics of the amoeba electronically using an analog circuit, as described in the journal Scientific Reports. “The amoeba core searches for a solution under the electronic environment where resistance values at intersections of crossbars represent constraints and requests of the TSP,” says Kasai. Using the crossbars, the city layout can be easily altered by updating the resistance values without complicated pre-processing.

Kenta Saito, a PhD student in Kasai’s lab, fabricated the circuit on a breadboard and succeeded in finding the shortest route for the 4-city TSP. He evaluated the performance for larger-sized problems using a circuit simulator. Then the circuit reliably found a high-quality legal solution with a significantly shorter route length than the average length obtained by the random sampling. Moreover, the time required to find a high-quality legal solution grew only linearly to the numbers of cities. Comparing the search time with a representative TSP algorithm “2-opt,” the electronic amoeba becomes more advantageous as the number of cities increases. “The analog circuit reproduces well the unique and efficient optimization capability of the amoeba, which the organism has acquired through natural selection,” says Kasai.

“As the analog computer consists of a simple and compact circuit, it can tackle many real-world problems in which inputs, constraints, and requests dynamically change and can be embedded into IoT devices as a power-saving microchip,” says Masashi Aono who leads Amoeba Energy to promote the practical use of the amoeba-inspired computers.

This is a Joint Release between Hokkaido University and Amoeba Energy Co., Ltd. More information

Data Skeptic Podcast

I had an opportunity to be one of the panellists in the Data Skeptic podcast recently. It was great to have been invited and as a listener to the podcast it was a really treat to be able to take part. Also, recording it was fun…

You can listen to the episode here.

More information about the Data Skeptic Journal Club can be found in their site. I would like to thank  Kyle Polich, Lan Guo and George Kemp for having me as a guest. I hope it is not the last time!

In the episode Kyle talks about the relationship between Covid-19 and Carbon Emissions. George tells us about the new Hateful Memes Challenge from Facebook. Lan joins us to talk about Google’s AI Explorables. I talk about a paper that uses neural networks to detect infections in the ear.

Let me know what you guys think!

Computer Programming Knowledge

I came across the image above in the Slack channel of the University of Hertfordshire Centre for Astrophysics Research. It summarises some of the fundamental knowledge in computer science that was assumed necessary at some point in time: Binar, CPU execution and algorithms.

They refer to 7 algorithms, but actually rather than actual algorithms they are classes:

  1. Sort
  2. Search
  3. Hashing
  4. Dynamic Programming
  5. Binary Exponentiation
  6. String Matching and Parsing
  7. Primality Testing

I like the periodic table shown at the bottom of the graphic. Showing some old friends such as Fortran, C, Basic and Cobol. Some other that are probably not used all that much, and others that have definitely been rising: Javascript, Java, C++, Lisp. It is great to se Python, number 35, listed as Multi-Paradigm!