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Undergraduate Robotics Research for Rural Appalachia

This NSF-Funded REU site will promote STEM opportunities through robotics-related research, education, and outreach. Undergraduate students will work in small groups in one of five robotics research labs at WVU under the mentorship of a faculty and perform research in the areas of motion planning, localization, coordination, autonomy, and locomotion for challenging field applications. The proposed research themes are tailored to problems associated with enabling the use of mobile robotics in rural environments that have challenging topography and/or are dense forested areas, typical to the Appalachian region. Participants will perform fundamental research leading to hands-on experimental demonstrations.  

Our educational objective is to provide opportunities for undergraduates to conduct independent research for problems that have great societal impact. The students will participate in weekly group research presentations, organized panel discussions, university sponsored workshops, team building activities, and a university-wide undergraduate research symposium. Participants will contribute to the state of the art within a specialized area of robotics, and faculty mentors will use group discussions to emphasize a holistic view of robotics systems and identify potential synergies and collaborations across sub-teams.

This REU site is supported by the National Science Foundation and builds upon our previous REU site on robotic swarms. The current site is directed by Dr. Jason Gross (PI) and co-directed by Dr. Guilherme Pereira (Co-PI)

The site focuses on recruiting a diverse set of participants, mainly from the Appalachian region, including students from underrepresented groups. All students, from all regions of the U.S., interested in robotics are welcome to apply. 


Enabling rural robotics and automation in the rural Appalachia will require new approaches which will be explored in several fundamental robotics research areas. The project areas will lead to innovation to address topics including: 1) natural swarm imitation 2) resilient drone motion in forests; 3) estimation methods for under-canopy localization; 4) low-energy strategies for multi-robot monitoring of environments with autonomous blimps; 5) decision-making approaches for driving in forest trails; and 6) new legged robots for agriculture applications. During the selection process, REU applicants will be requested to list their top three interests from these research areas. More information about each area can be found below. We also encourage applicants to explore each mentor’s website to learn more about their individual research activities.

1) Natural Swarm Imitation
Swarm of birds
Deployment of large teams of robots for farming or environmental monitoring in rural Appalachia can be made more fault-tolerant, scalable, flexible, and efficient if these are structured as robotic swarms. In this project, our first effort will focus on developing a simple swarm simulator and other computational tools for tuning simulated control laws to best fit a dataset. Data from simulated swarms will be used to design control laws composed of a sum of simple behaviors taken from a general library of functions. To test our controllers, a small-scale robotic swarm (e.g., a team of small drones) will be implemented and deployed under one of our motion capture systems. 
Mentors: Dr. Dimas Dutra and Dr. Guilherme Pereira

2) Resilient Drone Motion in Forests
Caged drone
Important management and preservation activities in forests rely on surveying large areas to detect invasive species, fire, and tree diseases. However, current surveying approaches are limited in scale by human resources; by safety, when is done with manned airplanes; and by accuracy, when satellite imagery is used. To overcome these limitations, the use of drones flying under the canopy of the forests has been suggested. However, flying in a forest is challenging both due to the large number of unmapped obstacles that need to be avoided and the presence of small flexible obstacles, such as leaves and twigs. To solve this problem, REU students will develop a resilient intelligent drone that can collide with obstacles to classify them and deal with such obstacles by avoiding or pushing them way.
Mentors: Dr. Guilherme Pereira and Dr. Cagri Kilic

3) Robust Drone Localization in Forests
Drone in a forest
Several applications, would benefit from the use of drones in forested regions. This is complicated under forest canopies because the availability and quality of the Global Navigation Satellite System (GNSS) are hindered by the signal attenuation of dense forests. On the other hand, this presents an interesting problem set-up because GNSS is not completely unavailable for use, and it can be made available when going above tree cover. In this project students will explore estimation strategies for improving localization of drones operating in forested regions under canopy. Given the nature of tree-cover with light shining through, students will, for example, explore the use of a fisheye camera in conjunction with GNSS signals as an input to predict signal quality, an approach which has shown some promise in urban setting.
Mentors:   Dr. Jason Gross and Dr. Cagri Kilic

4) Agriculture Monitoring with a Swarm of Blimps

Autonomous vehicles find great application potential in persistently monitoring farms. Existing solutions with quadcopters may deliver monitoring tasks efficiently but suffer from limited operation times due to the battery capacity. Monitoring a large farm would then require robot swarms, which consist of many autonomous mobile agents. More interesting is the case where the agents are low-cost robots that do not spend too much energy. This project will focus on the design and the development of swarms of autonomous blimps. Our current solution is to build helium-filled balloons that float in the air and are actuated with open-hardware propellers attached to the sides of the balloons. We aim to optimize the structure of the blimps, the locations, and the directions of the propellers to adjust the blimps’ poses in the air with minimum energy, such that the blimps can navigate through the air flow by leveraging the flow rather than fighting against it.
Mentors: Dr. Xi Yu and Dr. Jason Gross

5) Autonomous Driving on Hiking Trail
Robot in a trail

The research on autonomous driving so far has been mainly focused on paved roads and urban environments. Driving on common hiking trails offers such challenges: complex geography, unmarked boundaries, GPS degradation, interacting with pedestrians, bikers, and wild animals, changes of different time scales (e.g., from falling trees to growing bushes), weather conditions, among many others. Research in this direction, especially with physical robot testing in complex environments, will advance the state of the art in robot perception and decision-making methods. The REU research will focus on semantic outdoor mapping and terrain traversability assessment. The students will collect a large set of camera, LiDAR, GPS, Inertial, and wheel encoder data when driving a robot on hiking trails. They will perform real-time SLAM mapping and train image classifiers to identify objects along the way, such as trails, trees, rocks, and people. 
Mentors: Dr. Yu Gu and Dr. Cagri Kilic

6) Legged Farm Equipment for Sloped Fields
Legged robot

To increase mobility in rural, unstructured environments, legged robots are needed. Wheels perform best with infrastructure such as roads and rails, which do not exist in some rural environments. In crop fields, wheeled vehicles require wide gaps between rows of crops, which waste soil that could be used for growing and becomes compacted by tractors over time. Legged farm equipment would ameliorate these problems by replacing wide ruts with small “stepping stones” among densely planted crops, standing tall enough to have clearance over plants, and shifting their posture and center of mass to operate safely on slopes. To realize this vision, the energy efficiency and reliability of walking robots’ mechanics and control must be improved. This project will design new legs with a spring-actuator combination. This includes changing current models and testing this prediction through hardware tests in which actuators are subjected to impulse loads and the joint’s displacement over time is measured and characterized.
Mentors: Dr. Nicholas Szczecinski and Dr. Yu Gu

Program Dates:

May 20-July 26, 2024 (10 weeks in duration). 

Participant Benefits:

Stipend of $7,000 ($700/week for 10-weeks), lodging, meal expenses, travel reimbursement to/from REU Site (limited to ~$500/participant) and comprehensive training to move participants toward intellectual and research independence.


  • U.S. citizenship or permanent residency is required.
  • Students must be currently enrolled in an undergraduate program (no specific majors are required).
  • Students must not have completed an undergraduate degree prior to the summer program.
  • Students from any higher-education institution of the U.S. are eligible, but students from institutions in the Appalachian region are especially encouraged to apply.


Apply for the summer 2024 program by May 10, 2024. Applications will be evaluated as soon a received and on a rolling basis.  Applications will be accepted until all positions are filled.