Amanda Hashimoto

Papers

Total Citations

H-Index

About

Amanda Hashimoto is a leading researcher in human-robot collaboration for emergency response, specializing in autonomous systems for search and rescue operations. Her work bridges the critical gap between human decision-making and robotic autonomy, developing frameworks that enable unmanned aerial vehicles (UAVs) to work in tandem with ground-based human searchers. Her most cited paper, "Anticipatory Planning and Dynamic Lost Person Models for Human-Robot Search and Rescue" (2021, 22 citations), introduces a fully integrated planning framework that incorporates environmental data, lost person behavior models, and UAV path planning to optimize search efficiency. In her earlier foundational work (2020), Hashimoto pioneered a paradigm where autonomous UAV teams dynamically adapt their search patterns based on simulated lost person behavior models, allowing for more intuitive collaboration with human practitioners. Her research has direct implications for reducing search times in wilderness emergencies, natural disasters, and urban crisis scenarios. By treating the lost person as an active, behavioral agent rather than a static target, Hashimoto's models represent a significant advance in anticipatory robotics, positioning her as a key innovator in human-aware autonomous systems for life-saving applications.