Huai-Ti Lin

cropped-htl_header3.pngHuai-Ti Lin / Ph.D. in Biology

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Contact Huai-Ti (CV available upon request)

Studying biomechanics of soft-bodied animal locomotion

Huai-Ti Lin received his Ph.D. in January 2011 from Tufts University Department of Biology. His doctoral research focused on the biomechanics and behavioral adaptations in soft-bodied animal locomotion. His undergraduate training in experimental physics and biology at University of Massachusetts Amherst gave him a good basis for instrumentation and decent intuition of biomechanics. Lin devised a system to measure the ground reaction forces from a crawling caterpillar and revealed, for the first time, that soft-bodied animals use the ground as their external skeleton for force transmission. Lin expanded the investigation of this biomechanical strategy to its mechanism and implementation in nature. To understand how soft tissues distribute mechanical forces, he studied non-linear mechanics of soft materials and worked in the Mechanics of Soft Materials Laboratory at Tufts. To compare how different caterpillars exploit substrate support in nature, he conducted a field work in the tropical forest of Costa Rica and surveyed more than 80 caterpillar species.

Contributing to the new field of soft robotics

Tasked by his Ph.D. adviser Barry Trimmer, Lin helped establish a new interdisciplinary facility Tufts Biomimetic Devices Laboratory in collaboration with the Department of Mechanical Engineering and Biomedical Engineering. In the process, Lin received intensive hands-on training in mechanical engineering, electrical engineering and computational methods. Lin believed that a full understanding of biomechanics should allow one to implement the principles of a biomechanical system in a robotic platform. In the second half of his Ph.D., Lin tested his caterpillar gait-transition hypothesis in a crawling-inching soft robot he designed. The results demonstrated that inching can directly arise from the selective substrate attachment and a temporal compression of the crawling motor pattern. Lin also implemented the ballistic rolling escape behavior in some caterpillars on his soft robot GoQBot. This robot not only demonstrated one of the two self-powered wheeling locomotion in nature but also showed that body deformation can be beneficial in distributing body tension evenly for ballistic movements.

Moving on to animal flight research

Lin has moved on to animal flight research for his post-doctoral training. Flight has always been his passion so he was excited to expand his biomechanics research in the aerial direction. Since February 2011, Lin began his pursuit in animal flight research as a postdoctoral fellow at Harvard University, Cambridge, MA. Lin took up a project at Harvard Concord Field Station to look at obstacle negotiation in flying birds under the guidance of Andrew Biewener. He recorded 3D flight trajectories of pigeons flying through an obstacle course and reconstructed the visual stimuli. Combined with in-flight data from Lin’s various telemetry equipment for pigeons, his study pushed the avian flight tracking experiments to a new level. To extract guidance strategy from free-flight data, Lin developed new computational methods and modeled the flight trajectories based on reconstructed visual input. His model showed that pigeons use an intuitively simple strategy for short range obstacle flight by steering to the largest visual gap in the direction of flight velocity.

Focusing on flight control and the neural basis of visual guidance

To further his interest in visual guidance and flight control, Lin relocated to Howard Hughes Medical Institute Janelia Farm, where he joined Anthony Leonardo for the study of dragonfly prey capture behaviors. Lin began by designing a motion capture system that could triangulate the 3D kinematics of the dragonfly’s head and body orientation in flight. The data then allowed him to demonstrate how the eyes lock onto the target from the moment of prey detection. He then replayed these visual stimuli back to the dragonflies and recorded the responses from target-selective neurons in the nerve cord. These data brought major breakthrough on how dragonflies detect preys and make a pursuit decision. To verify the model, Lin engineered a robotic system to trick the dragonfly to pursue impossible preys. Lin’s other on-going projects include wireless neural recording during dragonfly flight, dragonfly conspecific pursuits, gliding flight control, and head control circuits. Ultimately, Lin wishes to piece out a complete story of visual guidance in animal flight from visual stimuli to flapping flight biomechanics.

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