A small team of University of Tulsa researchers will soon purchase a Barrett WAM® robotic arm thanks to a $137,446 grant from the National Science Foundation. Joining Associate Professor of Mechanical Engineering Joshua Schultz, the group’s principal investigator, are Professor of Mechanical Engineering Steve Tipton and Associate Professor of Anthropology Danielle Macdonald. The title of their project is Major Research Instrumentation: Acquisition of a Lightweight 7-Axis Robotic Manipulator with Force Sensing for Archaeological and Engineering Research and Education.
The Barrett WAM® robotic arm is one of the most sophisticated robot manipulators in existence, used by many of the world’s leading robotics research groups. While TU has had a robotic research group for nearly a decade, the lack of a robot arm has limited the types of experiments that could be performed and required extensive time and effort to build fixtures and jigs. “This arm will be a creative tool that allows TU researchers to conduct novel experiments that advance our understanding of robots and autonomous systems,” noted Schultz.
TU’s robot hand
The new arm is capable of moving a tool attached to its end to any position it can reach, at any angle. “We will use this arm to do experiments that move something over and over thousands of times, possibly changing it a little bit each time,” explained Tipton. One example he points to involves bending metal tubes back and forth to determine how many times they can bend before they break, knowledge of which is critical for determining when, for example, critical tubing systems should be removed from service.
The arm will also be used to position TU’s robotic hand so that researchers can study how to pick up and move objects with that hand. It will also be deployed to bump and push soft robots to see how they behave when they collide with other objects.
Experimental archaeology conducted by a human, but that will be undertaken by the robot arm in future
Outside of its applicability for engineering research and teaching, the Barrett WAM® arm will be a boon for researchers in archaeology. “In this regard,” commented Macdonald, “we can program the robot to use stone and bone tools the way prehistoric people did. The wear traces on experimental archaeological tools used by the robotic arm will be compared to wear traces on artifacts, which will allow us to gain deeper insight into the lives of ancient people.”
TU’s soft robot, Squishy, displaying a wrinkle (circled in red)
Associate Professor of Mechanical Engineering Joshua Schultz traveled to Philadelphia to attend and present recent research on soft robotics at the International Conference on Robotics and Automation (ICRA) conference. This gathering is the IEEE Robotics and Automation Society’s flagship conference. Because of the COVID-19 pandemic, it was Schultz and his collaborators’ first in-person presentation since receiving the National Science Foundation Emerging Frontiers grant in January 2020.
In an earlier paper published in Frontiers in Robotics and AI, Schultz and Postdoctoral Associate Peter (Phuc D.H.) Bui reported on the development of a semilinear parameter-varying observer (state estimator) they tested on an inflatable, fabric-reinforced soft robot named Squishy. Drawing on that work, for their ICRA presentation the duo, along with collaborators at Brigham Young University, focused on a method for dynamically simulating soft robots to predict their future movements.
Illustration of the mathematical relationships between two adjacent “discs” along the “thread” that joins them in the dynamic simulation environment
“The simulation environment presented at ICRA is really the flip side of the state estimator [described in the Frontiers article] coin,” remarked Schultz. “We shared with conference attendees our findings on how to simulate the movement of robots like ‘Squishy’ (TU’s inflatable fabric-reinforced tentacle robot), which allows us to predict where it will be in the future.”
Schultz and his colleagues expect that the new “disc-thread” simulation environment will better represent motions that occur within Squishy’s wrinkles, pleats and folds. By combining their earlier work with their more recent investigation, Schultz says they should be able to control the robot and drive it to a sequence of positions and shapes that will complete a useful task, such as cleaning or polishing a surface.
Meet Squishy and learn more about soft robotics and the related research going on at TU in this short video.
Joshua Schultz (right) at the ICRA conference with former student Tyler Morrison (BSME ’17), who recently earned a Ph.D. at Ohio State University
The research reported in this story is supported by NSF grant No. 1935312 EFRI C3 SoRo: Between a Soft Robot and a Hard Place: Estimation and Control Algorithms that Exploit Soft Robots’ Unique Abilities.
It’s hard not to be fascinated by robots! And there’s no better place for you to gain the knowledge, skills and networks you crave than TU’s Department of Mechanical Engineering.
The poses of the fabric-reinforced inflatable soft robot at four different pressures: (A) At 1.5 psi, (B) at 2.5 psi, (C) at 3 psi, (D) at 3.5 psi.
When most people think of robots, they likely envision rather hard-surfaced (often metallic), fairly durable machines. Researchers at The University of Tulsa, however, have designed and are conducting experiments with Squishy – a rather more delicate fabric-reinforced inflatable soft robot.
Postdoctoral Associate Peter (Phuc D. H.) Bui and Associate Professor of Mechanical Engineering Joshua Schultz recently published their latest soft robotics findings in Frontiers in Robotics and AI. In this article, the duo report on the development of a semilinear parameter-varying (SPV) observer (state estimator) they tested using Squishy.
What’s a soft robot?
As the name implies, a soft robot has a body made of soft materials, such as silicone. It is designed to do some tasks that a rigid robot cannot do, such as handling or touching fragile objects without damaging them or itself.
“Because of its soft and inflatable body, a soft robot is capable of safely and compliantly interacting with its surrounding environment,” explained Bui. “It can even survive a strong collision or falling from a great height.” With Squishy, a particular advantage is that it can be used to push an object using any part of its body, whereas a rigid robot typically interacts with its surroundings using some sort of tool at its tip.
Behaviorial evaluation for robots
Peter Bui
The SPV model Bui and Schultz discuss in their article is an advance on the current state of knowledge and practice in the field of soft robotics because it accounts for Squishy’s hysteresis (i.e., the motion when a robot follows two different trajectories: one when inflating and one when deflating). Other researchers have not considered this behavior in their models.
Robotics researchers such as Bui and Schultz often use the word “perception” when discussing the measurement of a robot’s “pose” (i.e., its shape and position in a 3D space).
But perceiving the pose of a soft robot in real time is challenging because its body is a continuum, unlike the body of a rigid robot, which is composed of well-defined links and joints. The normal way of measuring a soft robot’s pose is to employ a 3D motion-tracker system. But such a system is noisy, complicated and difficult to use in real time. To address this challenge, the TU researchers devised a state estimator for Squishy based on the output from a pressure sensor.
Bui and Phuc’s research has been assisted and broadened by graduate students Caroline Schell and Garrett Williamson, who have been instrumental in helping to build Squishy. The project’s co-principal investigator, Associate Professor of Mechanical Engineering Michael Keller, has contributed to creating its “smart material.”
The TU group is partnered with a team at Brigham Young University. There, co-principal investigator Marc Killpack has performed calculations and generated a Python simulation program to validate the SPV modeling approach. In addition, several Brigham Young undergraduate students have been involved in the research.
“Measuring a soft robot’s pose, velocity and acceleration is almost impossible using exclusively onboard-sensing devices,” explained Schultz. “Therefore, our work, which we discuss in the Frontiers in Robotics and AI article, entailed designing and testing an observer algorithm – the Sliding mode-based SPV observer — that can estimate all those states. Our model provides the information necessary to understand Squishy’s working status and supports the control process to generate correct control actions.”
Next frontier: Machine-learning
Joshua Schultz checks the tendons on the TU anthropomorphic robot hand
As they look to the future, Bui and Schultz are developing an embedded sensing system that can be used with Squishy in any environment. In particular, they are focused on creating a system for use when something in the robot’s environment pushes it off its natural free-space path and that can support a machine-learning algorithm to localize where that push is coming from.
“We also envision that the new sensing approach and observer designed in this research will be combined to serve the controller. The result of this will be increased reliability and the ability for the robot to perform intelligent behavior,” remarked Bui.
The research reported in this story is supported by NSF grant No. 1935312 EFRI C3 SoRo: Between a Soft Robot and a Hard Place: Estimation and Control Algorithms that Exploit Soft Robots’ Unique Abilities.
If robots – soft and hard – fascinate you, there’s no better place to gain the knowledge, skills and networks you need than TU’s Department of Mechanical Engineering.
Imagine a search and rescue situation where a robot combs through rubble to save a trapped individual or an agile robot is administered to detonate a bomb. The machines ideal for these types of scenarios are soft robots that can be built with a higher level of mobility at a much lower cost. Soft robots hold enormous potential to save lives and improve manufacturing, and the TU Department of Mechanical Engineering is adding this type of project to its current list of research initiatives.
Just announced with an official start date of January 2020, a National Science Foundation Emerging Frontiers & Multidisciplinary Activities grant worth $1.9 million has been awarded to Associate Professor Joshua Schultz and his biological robotics team. The NSF funding is the largest award Schultz has received in his career as a principal investigator and will support efforts to improve the mobility and control of fabric-reinforced inflatable soft robots. When applying for the grant, Schultz recruited the expertise of materials scientist and TU Associate Professor of Mechanical Engineering Michael Keller. Schultz and Keller are teaming up with two co-principal investigators from Brigham Young University: Assistant Professor of Mechanical Engineering Marc Killpack and Assistant Professor of Computer Science David Wingate. Schultz said robots built at TU and soft robotics research underway at BYU will explore functions and capabilities that aren’t possible in traditional models. “Soft robots can bump, scrape and push against the world,” he explained. “They can accomplish tasks by running into a wall on purpose and then using the wall to support itself when it reaches for something.”
Autonomous robots
Professor Joshua Schultz
The concept of soft robotics also involves devices that can reach under a door to retrieve an item, entwine objects or squeeze into a small gap between a door that’s ajar. Soft robots can conform to features in an unknown environment and change shape, and when they collide with another object, neither the robot nor the other item is damaged. Equipping the device with smart technology allows it to detect that contact occurred and behave intelligently to complete the task. “We are trying to make these robots autonomous, so they can operate on their own without a human continuously issuing commands in real-time,” Schultz said. “Previous soft robots needed the ingenuity of a human to complete the tasks that the robot was doing. We will use new mathematical models, smart materials and sensors, and machine learning, so that robots can do the task autonomously without supervision by a human.”
The faculty plan to host graduate students at both TU and BYU to assist with the project and add a TU post-doctoral fellow to conduct research in kinematics and materials science. For the next four years, the group will focus on developing concise models for the motions of a fabric-reinforced rubber tube that can be evaluated quickly by a computer. Schultz described the device as an arm made of silicone. One side of the arm is made from a fabric similar to the canvas of a tent while the other is constructed of a more flexible material. The arm’s fabric can set up, bend, move and fold, but it won’t stretch. As the arm inflates, the stretchy side stretches but the fabric side will only bend. “As this robot is made, it can only trace out one path in space,” Schultz explained. “It can’t reach robotics workspace, but we want the robot to be pear, ring or cone in shape — we want it to have some volume in which it can reach everywhere inside its range of motion.”
Adapting to workspace with smart technology
Keller and Schultz are experimenting with materials that can be patched to the arm and communicate with a computer. By turning on one patch or a combination of patches at different times, the arm’s wall stiffness will change and expand its potential workspace. “The robot will be able to reach in some useful volume to do tasks, so that’s progress in the device functioning like a robot instead of just a conversation piece,” Schultz said.
Once the robot can reach a given volume, it must move to a specific spot within that space, so controlling movement and alleviating bounce from the stretchy material is key. Schultz and Keller hope to change the stiffness and inflation of the robot’s walls in a way that if it is bumped and begins to bounce, they can stop the interference and smoothly move it to pick up something or move against something. “Robotics is used in all kinds of industries, but the reason robots are limited in their application is because they can easily bump into something, and they lack dexterity to do a task,” Schultz explained.
Scientists and engineers have successfully demonstrated soft robotics designs in the past but adding the component of smart technology poses a challenge. Schultz said the elastomeric materials of soft robots allow for many sensors to be molded into the robot to measure touch, proximity and the shape of objects. If successful, the project will produce improved algorithms to process sensor inputs and enable the robot to distill them into meaningful information about itself and the world. Schultz said that while the designs they plan to develop are technologically advanced, they are financially feasible. “An industrial robot might cost upwards of $20,000, and you have to make a big purchase before you can find out if it suits your company’s needs,” he said. “Soft robots are more like disposable income — they’re made from pretty inexpensive materials.”
Overcoming challenges with soft robotics
Basic models developed by the team will present an algorithm that will configure a variety of possible shapes for the robot at each instant. Data gathered by the platforms will help the soft robot learn how to select the appropriate commands, which include the combinations of pneumatic valve signals and tunable stiffness patches in the rubber walls, to autonomously complete useful tasks where the robot must push on the environment.
“We want our robot to be able to overcome challenges like hanging drywall and reaching into clutter amongst trees, tall grass, rocks or debris to bend around anything in its path,” Schultz said.
To learn more about biological robotics at The University of Tulsa, please contact Professor Schultz at joshua-schultz@utulsa.edu.
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