For the last few decades the state of the art has been evolving toward agile autonomous vehicles. Limitations in performance in available instrumentation, “algorithms,” and computational hardware have resulted in a nested control loop structure separated by loop frequencies: an “inner loop” which runs at a relatively high rate and is used to maintain vehicle stability (typically instrumented with an IMU), and an “outer loop” that typically runs at lower frequency and utilizes “exteroceptive” sensors such as seekers.
Recent advances in the studies of motion detection and control in flying animals, including the insight that animals sense differences rather than absolutes (Taylor and Krapp 2008), coupled with advances in sensors, algorithms, and data processing, suggest the possibility of developing agile autonomous vehicles with simpler control than current state of the art, and higher performance. Studies of the mechanisms allowing animals to exploit multiple sensory modalities to achieve high performance in real-world, complex, high-uncertainty environments offer tantalizing possibilities for adding comparable levels of robust performance to engineered agile autonomous vehicles (Kelber, Gilmour, Sane 2026).
We are interested in candidates who share this viewpoint: use principles from biology with emerging technology advances to enable design and demonstration of such vehicles. Fast mechanical sensors such as strain gauge arrays, motivated by use of arrays of campaniform sensilla in insects, various flight control sensors suggested by Johnston’s organ, novel sensors without identified biological examples such as pressure field arrays are of interest for “inner loop” sparse sensing. Sparse and wide field integrated array configurations are of interest.
Fast capable optical sensors for situational awareness, optical flow sensing, object identification and tracking are of interest. Principles extracted from biology, manifest in multispectral, polarization sensing, wide field of view imaging systems configured as event-based sensors could provide traditional outer loop sensing but at much higher sample rates than traditional, read-out-the-entire-focal-plane-at-fixed-rate imaging sensors. Sufficiently fast outer loop sensing could obviate the distinction between the classical inner and outer loops. Sufficiently high sensing rates of a sufficiently well-equipped sensor suite can reduce computation complexity and thus enable much faster processing using control algorithms peculiarly suited to fast sensing, such as incremental nonlinear dynamic inversion (Smeur et al, 2016).
We are interested in the use of strategies identified in the animal kingdom such as matched filters, sparse sensing, wide field integration, as well as use of direct measurements such as listed above, to achieve a new generation of truly agile autonomous vehicles. We have two primary interest areas: (1) detailed modeling of advanced design, following and extending models for airframes with advanced sensors and control strategies, including verifying and extending airframes with advanced sensors developed for us under contract, with analysis such as suggested in Humbert et al (2026) and Turin et al (2025), and (2) focus on investigating, in the laboratory, sensors found on insects and determined to be used for flight control, including mechanosensors and vision sensors, to enable developing advanced sensors for advanced flight control.
Key words: guidance, navigation, control, INDI, mechanosensor, event-based sensing
References:
F. G. Barth and A. Schmid, Ecology of Sensing, Springer, 2001
T. W. Cronin, S. Johnsen, N. J. Marshall, E. J. Warrant, Visual Ecology, Princeton, 2014
J. S. Humbert, G. K. Taylor, H. G. Krapp et al, Fly motion vision maximizes signal energy transfer between mechanical input and sensor output, Science Robotics 11 (112), 2026.
A. Kelber, K. M. Gilmour, S. P. Sane, Sensory biology in a changing world: multisensory systems and interdisciplinary collaboration, JEB 229, 2026
H. G. Krapp, G. K. Taylor, J. S. Humbert, The mode sensing hypothesis: matching sensors, actuators, and flight dynamics, chapter 7 in F. G. Barth, J. A. C. Humphrey, and M. V. Srinivasan, Frontiers in Sensing: from biology to engineering, Springer, 2012
G. K. Taylor and H. G. Krapp, Sensory Systems and Flight Stability: What do insects measure and why? in J. Casas and S. J. Simpson, Advances in Insect Physiology vol 34: Insect Mechanics and Control, Academic Press, 2008
E. J. J. Smeur, Q. Chu, G. H. E. de Croon, Adaptive Incremental Nonlinear Dynamics Inversion for Attitude Control of Micro Air Vehicles, Journal of Guidance, Control, and Dynamics, vol 39, no. 3, March 2016
Z. Turin, G. K. Taylor, H. G. Krapp, E. Jensen, J. Sean Humbert, Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures. IEEE Access 13, 13584–13605 (2025).
R. Wehner, 'Matched filters'- neural models of the external world, Journal of Comparative Physiology A, 1987
guidance and control; inner loop; outer loop; bioinspired; mechanosensor; event-based sensing; INDI
Additional Benefits
relocation
Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.
health insurance
A group health insurance program is available to awardees and their qualifying dependents in the United States.