Hi! My name is Sophia Schiffer, and I am a rising sophomore studying Mechanical Engineering at Northwestern University. Earlier this summer, I assisted in the design process and created a demonstration for the soft robotic gripper developed by my mentor Dr. Jie Xu’s research team. Then, I transitioned to working on the control system and interfacing of the gripper under the guidance of Jie Xu and Dr. Chengshi Wang. I will continue to work on this objective for the remainder of the summer. The goal of the soft gripper project is to construct a “hand” that will provide general assistance in many applications where people and workplaces can benefit from the delicate touch and handling capabilities of soft robots.
The concept of the soft robot emerged recently, only in the last decade. This technology presents a major shift in the field of robotics. In the past, robots were constructed almost exclusively with rigid materials, such as steel, aluminum, and plastic. These hard components are usually electronically actuated, adding failure modes to some robotic applications, such as marine robotics where all electronic components must stay dry for the robot to function. Additionally, rigid grippers are very precise. With intensive machine learning, they can acquire the skill of grasping a specific object. However, when the object changes, the robot must relearn to pick up the new object from scratch.
Soft grippers revolutionize robotics for packaging and pick and place applications because they only have two states: open and closed. These grippers are made from delicate materials, often silicone, which will not damage items at full grip strength. This allows the gripper to close around an object of any shape by simply squeezing as hard as it can and allowing itself to adapt to the shape of the item. Use of softer materials in robotics also strives toward minimizing injury in shared human-robot workspaces.
Soft Gripper at CNM
The team I am working with under Jie Xu at the Center for Nanoscale Materials (CNM) is developing a soft gripper that goes beyond the capabilities of the basic grippers widely used in packaging and similar industries today. The fingers on this gripper are unique in the way that they can not only bend, but also expand, getting longer and narrower.
For a soft finger to bend, air is pumped into one side of the finger, the side opposite the bending direction, while the other side remains unchanged. When one side of the finger expands to be longer than the other, this naturally forces a curvature in the finger. Our team’s design has three channels, two of which are filled with air to bend the finger (see Figure 1). Stretching the fingers can be accomplished by pressing air into all three channels simultaneously. The way to realize this expansion is to make sure that all three air channels only expand in the direction in which the finger points, not laterally. At this point, the team is still in the early stages of testing non-expanding, bend only fingers. Once these fingers are fully finalized, I look forward to trying and iterating the design of the expanding fingers until they are fully capable of squeezing into narrow openings and stretching to impressive lengths.
During the first four weeks of my internship, I was tasked with constructing a demonstration for the first iteration of the gripper, Design #1 (see Figure 2). The demonstration I designed highlights the advantages of soft fingers which can stretch and squeeze into narrow openings. Performing this demonstration with a hand-shaped gripper body presented many challenges, however. Among these were making the palm shrink as well as the fingers, or alternatively designing the thumb such that it could stretch even further than the other fingers so that the palm would not have to fit through a narrow tube or opening. In anticipation of these obstacles, I proposed an octopus-inspired gripper design. Based on this idea, another NAISE intern Louis Wong crafted Design #2 (see Figure 2). With the new design, I constructed a demonstration in which the gripper’s soft fingers must fit into a narrow opening. Once inside, the fingers touch unknown objects with multichannel soft sensors to categorize the mystery items. This is done entirely without visual aid, showing how the extendable fingers accomplish the goal of assisting humans by going beyond human capabilities.
Haptic Identification and Closed-Loop Control
My demonstration highlights the advantage of a robot capable of identifying objects without relying on visual data. Soft grippers can identify objects haptically by applying pressure to the objects’ surfaces. Sensors embedded onto the surface of the soft fingers measure the amount of pressure required to create a slight deformation in the object’s surface, indicating the softness of the object. My team’s grippers will implement multichannel sensors, which include pressure sensing. Working with Chengshi Wang, I will develop the Python codes to program the closed-loop control system for Design #1. Most soft grippers use an open-loop system. Here, the computer tells the gripper to perform a simple task, such as open or close, and receives no feedback from the gripper itself. In a closed-loop system, the code indicates how much air should be pumped into the channels within the fingers. As a result, the soft fingers apply a measurable amount of pressure onto the object they are gripping. The sensors send this pressure data back to the computer which then adjusts the amount of air to pump into the channels based on a set desired value for surface pressure. For now, this feedback system will allow our gripper to apply the perfect amount of pressure to objects to pick them up and manipulate them with a reduced risk of damaging the items. After further development, the multichannel sensors will also assist in haptic object identification.
The initial control system will use Arduino hardware, wired as shown in diagram below (see Figure 3). To correctly regulate pressure into the soft fingers, I will use the Arduino IDE software in conjunction with the pySerial module of Python. Using Python enables collecting data from the fingers’ sensors, adjusting pressure setpoints, and sending these new inputs to the pressure regulators all within the same program.
During the rest of my ten-week internship with NAISE, I will be focusing on coding the control system for one of the fingers in Design #1 (bending only). Before my final presentation, my and Louis Wong’s goal is to have one finger printed, functional, and programed such that it can sense an object by touching it. To demonstrate this, I will code the finger to retract from the object once it “feels” it. Once the code for a single finger is complete, the team will be able to apply it to the other four fingers so they may work together to pick up and place delicate objects. Following the internship, I will work on improving Design #2, researching inspirations from nature. I will contribute to the coding of the rest of the fingers in Design #1 and hopefully also work on the control system for the second gripper, which will be used in the demonstration that I created. I am excited to continue working on designing and developing code for both the team’s grippers. I am very grateful for the mentorship of Dr. Jie Xu and additional guidance I received from Dr. Chengshi Wang which allowed me to learn and gain valuable experience in my time with NAISE so far. The potential of soft robotic technology far surpasses that which has already been explored. I am hopeful that the technology I have been working on this summer will improve the quality of life for some, and the workplace for others, in the near future.
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