Jasmine Cashbaugh

Santa Clara University, University of Auckland

Papers

Total Citations

H-Index

About

Jasmine Cashbaugh’s research lies at the intersection of multirobot coordination, autonomous aerial systems, and agricultural robotics. Her most impactful work presents a novel strategy for vision-based object tracking using an optimally positioned cluster of mobile tracking stations, demonstrating how a multirobot network can dynamically reconfigure itself to minimize target estimation error—a contribution that has earned 14 citations and stands as her most referenced paper. Building on this foundation, she contributed to the development of a quadrotor testbed at NASA Ames, advancing aerial robot cluster control, a domain previously underexplored in her lab. Cashbaugh also applied her systems-thinking to agricultural automation, evaluating a spray scheduling algorithm for variable-rate liquid application on robotic booms, a project funded by the MBIE Multipurpose Orchard Robotics contract. Her work bridges theoretical optimization with practical deployment across ground and aerial platforms, showcasing a rare versatility in robotics. With a career that spans multi-agent tracking, UAV testbed development, and precision agriculture, Cashbaugh exemplifies how rigorous algorithmic design can solve real-world challenges in dynamic, unstructured environments.