Trevor W. Exley

University of North Texas, Discovery Park

Office E225G, Lab C245

Email: trevorexley[at]my.unt.edu

As a Ph.D. candidate at the University of North Texas, I’m engaged in cutting-edge research at the Advanced Robotic Manipulators (ARM) Lab, where my focus lies on the innovative realms of thermo-active soft actuators and variable impedance actuators. This work is at the forefront of advancing robotic capabilities, blending principles from various disciplines to push the boundaries of what’s possible in robotic manipulation and actuation.

My journey into robotics builds upon a strong foundation laid during my master’s program, where I was affiliated with the Biomedical AI Lab at UNT. There, I delved into the potential of sensor data to uncover predictive capabilities for Parkinson’s symptoms, a project that stands at the intersection of healthcare, machine learning, and robotics.

My current research endeavors center around:

Developing Thermo-Active Soft Actuators: Exploring innovative materials and designs that respond to temperature changes, enabling new forms of movement and interaction in robotic systems.
Advancing Variable Impedance Actuators: Creating actuators that can dynamically alter their stiffness and damping properties, allowing robots to adapt to a wide range of tasks and environments with unprecedented versatility.
Interdisciplinary Applications: Bridging the gap between robotics and other fields, such as biomedical engineering and artificial intelligence, to address complex challenges with holistic, innovative solutions.

Through my work, I aim to not only contribute valuable insights and advancements to the field of robotics but also to pave the way for applications that improve human life and interaction with technology.

selected publications

2024

Toward a Unified Naming Scheme for Thermo-Active Soft Actuators: A Review of Materials, Working Principles, and Applications

Trevor Exley, Emilly Hays, Daniel Johnson, Arian Moridani, Ramya Motati, and Amir Jafari

Robotics Reports, Jan 2024

Abs

Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as “intelligent,” “smart,” and “adaptive” materials, which add vagueness to the broad innovation among literature. Therefore, a clear, functional, and descriptive naming scheme is proposed in which a previously vague name—Soft Material for Soft Actuators—can remain clear and concise—Phase-Change Elastomers for Artificial Muscles. By synthesizing the working principle, material, and application into a naming scheme, the searchability of soft robotics can be enhanced and applied to other fields. The field of thermo-active soft actuators spans multiple domains and requires added clarity. Thermo-active actuators have potential for a variety of applications spanning virtual reality haptics to assistive devices. This review offers a comprehensive guide to selecting the type of thermo-active actuator when one has an application in mind. In addition, it discusses future directions and improvements that are necessary for implementation.

2023

Toward a Novel Thermal-Based Variable Impedance Module Through Adjusting Viscoelastic Properties of a Thermoresponsive Polymer

Trevor Exley, Daniel Johnson, and Amir Jafari

IEEE Transactions on Medical Robotics and Bionics, Nov 2023

Abs

In this article, we propose a one-of-a-kind thermal-based variable impedance module to be used towards developing a variable impedance actuator. Unlike other variable impedance actuators, where the impedance adjustment modules employ a mechanism to regulate their impedance, this novel module does it through controlling temperature of a thermoplastic polymer Polycaprolactone. The viscoelastic properties of polycaprolactone are temperature-dependent, increasing the rigidity when it is cooling down and softening when it is heating up. To change the temperature, thermo-electric Peltiers are embedded into the design. Preliminary experiments have shown that this module is able to adjust its impedance through changing the temperature. The simplicity and lightness of the proposed off-line impedance adjustment approach make it a suitable choice when the actuator’s size, weight, and compactness are the main concerns. This is because the proposed design is highly scalable, and by scaling down the size of the module, its performance regarding the speed of impedance adjustment would increase.
A Novel Variable Impedance Actuator Utilizing Adjustable Viscoelastic Properties of Thermoresponsive \emphPolycaprolactone

Trevor Exley, Daniel Johnson, and Amir Jafari

Robotics Reports, Nov 2023
Utilizing the Peltier Effect for Actuation of Thermo-Active Soft Robots

Trevor Exley, Daniel Johnson, and Amir Jafari

Smart Materials and Structures, Jul 2023

Abs

The field of soft actuation methods in robotics is rapidly advancing and holds promise for physical interactions between humans and robots due to the adaptability of materials and compliant structures. Among these methods, thermally-responsive soft actuators are particularly unique, ensuring portability as they do not require stationary pumps, or high voltage sources, or remote magnetic field. However, since working principles of these actuators are based on Joule heating, the systems are inefficient and dramatically slow, especially due to their passive cooling process. This paper proposes using the Peltier effect as a reversible heating/cooling mechanism for thermo-active soft actuators to enable faster deformations, more efficient heat transfer, and active cooling. The proposed actuator is composed of a thin elastic membrane filled with phase-change fluid that can vaporize when heated to produce large deformations. This membrane is placed in a braided mesh to create a McKibben muscle that can lift 5 N after 60 s of heating, and is further formed into a gripper capable of manipulating objects within the environment. The effectiveness of the proposed actuator is demonstrated, and its potential applications in various fields are discussed.

2022

Predicting UPDRS Motor Symptoms in Individuals With Parkinson’s Disease From Force Plates Using Machine Learning

Trevor Exley, Sarah Moudy, Rita M. Patterson, Joonghyun Kim, and Mark V. Albert

IEEE Journal of Biomedical and Health Informatics, Jul 2022

Abs

Parkinson’s disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinson’s Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e., the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.