I study how animals move and look at biology for inspiration to develop better insect-scale robots. In animals, I study the interactions between the mechanics of their body parts and their nervous system within natural or virtual environments. I’m interested in extracting general principles of animal locomotion at multiple scales, from individual neural networks, to muscles and to whole body motion. For example, how do organisms cope with sensory conduction delays when moving rapidly through the world? What is the role of mechanics in sensing? I study these problems by employing a range of techniques in biomechanics and neurophysiology while integrating engineering tools (e.g. robotics, control theory) and taking into consideration the ecological and evolutionary contexts of the organism. To date I have studied high-speed tasks in amazingly maneuverable animals: cockroaches and fruit flies.
A central challenge in reverse engineering locomotion is that the control system of animals is embedded within both the neural circuits and the mechanics of the body. These systems are dynamically coupled, governed by the biophysics of neural networks and physical laws of motion. Because locomotion is inherently “closed-loop”, it is difficult to reverse engineer without a framework that can quantify system-level interactions. To address this challenge in biology, I use mathematical/engineering tools to make predictions about the underlying organization of animals’ control system and test these predictions using tools in biomechanics and neuroscience.
I. Locomotion-mediated sensing
When animals are on the move, their own body motion propels sensors distributed on the body. Therefore voluntary movement inescapably stimulates the same sensors that detect involuntary perturbations to an organism’s heading. In some cases, self-generated movements can be exploited for more effective sensing, for instance, by generating Coriolis forces to stabilize flight. However, self-generated movement mixes sensory inputs due to self-motion (reafference) and external causes (exafference) on the same channel. Differentiating between reafferent and exafferent sensory input is a critical problem that the nervous system of mobile organisms must resolve.
Locomotion- and mechanics-mediated tactile sensing
While it has been established that flight can drive sensors for more effective control (e.g. antennae, halteres), it is less understood how locomotion and sensor biomechanics could make control more effective during terrestrial locomotion. I discovered that during tactilely mediated course control, cockroaches can rely on the passive properties of their antenna to exert control over this sensor’s mechanical state. I showed that at the level of the body, the sensor reconfiguration is an active process, but that at the level of the sensor, it is a passive process relying on the mechanical interlocking of hairs into surfaces that reconfigure the antennae into a more effective shape.
Mongeau, J.-M., Demir, A., Lee, J., Cowan, N.J., Full, R.J. (2013) Locomotion- and mechanics-mediated tactile sensing: antenna reconfiguration simplifies control during high-speed navigation in cockroaches. Journal of Experimental Biology. DOI:10.1242/jeb.083477(PDF)
As a tactile sensor is a physical linkage mediating mechanical interactions between body and environment, mechanical tuning of sensor is critical for effective control. I studied mechanical properties of the primary tactile sensors of cockroaches, the antennae, using a combination of experimental, computational and robotic techniques. I revealed how both the static and dynamic properties of the antenna influence sensory acquisition during quasi-static and dynamic sensorimotor tasks. This work has led to the design of biologically-inspired sensor to study the interaction between mechanics and sensing and this sensor is currently being interfaced to a mobile robot for tactile navigation.
Mongeau, J.-M., Demir, A., Dallmann, C.J., Jayaram, K., Cowan, N.J., Full, R.J. (2014) Mechanical processing via passive dynamic properties of the cockroach antenna can facilitate control during rapid running. Journal of Experimental Biology. DOI: 10.1242/jeb.101501 (PDF)
II. Feedback Control through Neuromechanical Processing
As animals operate in closed-loop, it is often challenging to predict what neural information may be required for controlling behavior without an understanding of body and sensor dynamics. Engineering control theory provides a framework that allows me to quantitatively integrate sensing and mechanics and to probe these questions directly. Sensory neurophysiology often reveals what information can be encoded by neurons, thereby revealing the broader capability of biological sensors. By using a neuromechanical framework, my research will go a step further by inquiring what neural signals may be required for controlling animal behavior.
Sensory processing to implement feedback control during running
As the speed of locomotion increases, neural bandwidth and processing delays can limit the ability to achieve and maintain stable control. Converting sensory information into a control signal within the sensor itself could enable rapid implementation of whole-body feedback control during high-speed locomotion. By integrating neural hypotheses within an engineering framework, I discovered a cellular control circuit for stabilizing high-speed locomotion in cockroaches, demonstrating that neural processing within a sensor is tuned to implement whole-body feedback control. Specifically, by direct neural recording from individual sensory cells, I discovered that within the antenna, the distributed arrays of mechanoreceptors have distinct latencies and filtering properties that when summed, generate a control input tuned for high-speed steering.
Mongeau, J.-M., Sponberg, S.N., Miller, J.P., Full, R.J. (2015) Sensory processing within antenna enables rapid implementation of feedback control for high-speed running maneuvers. Journal of Experimental Biology DOI: 10.1242/jeb.118604 (PDF)
III. Movement Control under Perturbations
Animal locomotion is inescapably closed-loop: motor outputs feedback onto sensory inputs. The interactions between neural and mechanical systems within this loop are often difficult to study and quantify when animals operate in steady state. In contrast, when animals are perturbed to challenge their stability, there is a greater performance demand on the physiology which in turn can expose the interplay of mechanical and neural systems.
Flies temporally integrate visual signals to control flight maneuvers
Flies fly through the world by generating smooth movement and body saccades, similar to the way our own eyes move. Body saccades are ballistic maneuvers that enables flies to control their gaze by rapidly turning their body. By suspending flies magnetically in virtual reality, I showed that flies precisely control the dynamics of saccades by temporally integrating visual signals generated by a moving object. Saccades allow flies to rapidly fixate objects, which would be useful when searching for a potential landing site. This study revealed that simple algorithms underlie the trigger and control of fixation saccades.
Mongeau, J.-M., Frye, M.A. (2017) Drosophila spatiotemporally integrates visual signals to control saccades. Current Biology DOI: http://dx.doi.org/10.1016/j.cub.2017.08.035
Neuromechanics of agility in small animals
Escaping from predators often demands that animals rapidly move in complex environments. Small animals benefit from the advantages of enhanced maneuverability in part due to scaling. I discovered that small legged animals can disappear from predators by running rapidly at 12-15 body lengths-per-second toward a ledge without braking, dive off the ledge, attach their feet by claws like a grappling hook, and use an active pendular motion that can exceed one-meter-second to swing around to an inverted position under the ledge, out of sight. By forming a team of biologists and engineers, the rapid inversion behavior inspired the design of a legged robot that begins to demonstrate this rapid inversion capability.
Mongeau, J.-M., McRae, B., Jusufi, A., Birkmeyer, P., Hoover, A.M., Fearing, R., Full, R.J. (2012) Rapid inversion: running animals and robots swing like a pendulum under ledges. PLoS ONE. DOI:10.1371/journal.pone.0038003 (PDF)