AI-powered multimodal precision sensing system to track tick phenotypic responses in natural and simulated environments - the long-term goal of our team (DRS

The National Institute of Allergy and Infectious Diseases awarded Purdue University $415,584 on April 1, 2026, under the Allergy and Infectious Diseases Research program (CFDA 93.855, Project Grant R21AI188198) to develop an AI-powered autonomous robotic system for detection and targeted treatment of Ixodes scapularis (Lyme disease tick) in natural environments.

The award funds a two-year project running through March 31, 2028, with performance at West Lafayette, Indiana. The research combines tick biology, artificial intelligence, sensing, and robotics to introduce two novel technologies: a millimeter-wave sensing system customized for detection and tracking of multiple tick nymphs and adults in occluded environments, and AI-based algorithms to drive autonomous, large-scale sampling and detection of tick habitat by quadruped robots or drones.

Aim 1 develops an AI-powered multimodal sensing system leveraging the Purdue Phenocosm test arena to assess tick behaviors over extended study periods across various substrate types mimicking natural tick habitats. The system combines millimeter-wave and traditional camera technology with neural network-driven tracking algorithms to enable continuous monitoring of multiple ticks in complex, occluded environments.

Aim 2 develops path planning, navigation, and control algorithms supporting autonomous quadruped robot movement in outdoor settings, including deciduous forest and grass ecotones that support I. scapularis populations. The system enables the robot to navigate varied terrain and adapt movements while carrying out detection and sampling capabilities.

Hill, murgia and kaur) is to revolutionize control of vector-borne diseases through innovations in autonomous, AI-driven robotics systems for precision pest management. On this two-year R21, we propose the development of a transformative autonomous robotic system for precise, real-time detection and targeted treatment of the lyme disease tick, ixodes scapularis in occluded natural environments. We will leverage our combined expertise in tick biology, AI, sensing, robotics and product development to introduce two novel technologies: (1) a mmwave-sensing system customized for detection and tracking of multiple nymphs and adult ticks in occluded environments and (2) AI-based algorithms to drive the autonomous, large-scale sampling of multiple land types, and subsequent detection of tick habitat and tick hotspots by quadruped robots (or alternatively, drones). In aim 1, we will develop an AI- powered multimodal sensing-based system taking advantage of the purdue phenocosm, a versatile test arena for detailed assessment of tick behaviors over weeks-long study periods and involving various substrate types to mimic natural tick habitats. This work will combine multi-modal mmwave and traditional camera technology, together with neural network-driven tracking algorithms. Through this work, we expect to generate the first system capable of continuously monitoring multiple ticks in complex, occluded environments, representing a paradigm shift for surveillance. In aim 2, we will develop sophisticated path planning, navigation, and control algorithms needed to support autonomous quadruped robot movement in outdoor settings, including a range of habitats such as deciduous forest/grass ecotones that typically support i. Scapularis populations. The quadruped robot will have capability to (1) navigate in and adapt movements for uneven and complex terrains, (2) optimize movement for energy efficiency, (3) collect large environmental data sets and (4) detect ticks concealed by vegetation, leaf litter and other matter. The sensing and robotics technologies developed on this project will lay the foundation for future field studies to validate protypes for area-wide tick surveillance and targeted control. Once validated and fully developed, this autonomous system will greatly expand U.S. Public health surveillance capacity, substantially reducing human-tick encounters, the incidence and burden of tick-borne diseases, pesticide use, healthcare and pest management costs, paving the way for a new ERA in fully remote pest and disease management. 1