Mechanical engineering student and softball player Lael Ayala created an autonomous robot for her senior thesis that detects and collects softballs. Her project not only reflects her passion for both engineering and athletics but aims to save valuable practice time that athletes often spend shagging balls. Guided by Professor Seymur Hasanov, Ayala utilized machine learning and robotics principles to develop a cart-like robot that collects an average of 6.5 softballs per session.

By the Numbers
  • Ayala's robot collects an average of 6.5 softballs per testing session.
  • She utilized hundreds of photographs to train the robot using machine learning.
State of Play
  • Ayala balances her time between athletics, engineering, and Army ROTC commitments.
  • She applied her leadership skills gained from ROTC and softball to enhance her engineering project.
What's Next

Following her graduation, Ayala will attend Army officer training while also focusing on her startup, Gander Robotics, which develops autonomous underwater drones for search and rescue operations.

Bottom Line

Ayala's innovative approach not only showcases the integration of sports and engineering but also highlights the potential for technology to enhance athletic training, setting a precedent for future advancements in sports-related robotics.