![]() ![]() However, all manipulations that altered the antennal system changed behavior in a way consistent with the hypothesis that antennal mechanosensation plays a major role in collision avoidance. Tests for the use of vision in detecting obstacles showed that its role, if any, is small. The avoidance strategies chosen depended on the size and configuration of the obstacle. However, occasionally animals moved vertically and climbed over the barrier, or turned and navigated an edge of the obstacle, or completely reversed run direction. The most common collision avoidance strategy was simply to stop running prior to impact. Intact cockroaches collided with obstacles on only about 10% of trials. The goal was to determine the repertoire of possible responses to obstacles and the sensory cues used to trigger the responses. Finally, we integrated the sensor and controller on Sprawlette and showed empirically that stabilizing Sprawlette during wall following does indeed require tactile flow, as predicted.Ĭockroaches were observed with videographic methods as escape running was initiated, but with obstacles in the path of their run. Based on these steps, we designed and calibrated a prototype tactile sensor to measure Sprawlette's angle and distance relative to a straight wall, and employed a simple bio-inspired control law that can stabilize the template dy- namics. Second, we collected initial cockroach data that supports the idea that the rate of convergence to the wall or "tactile flow" is being used, in part, for controlling body orientation. First, we developed a simple template model for antenna-based wall following. To bridge the gap between biology and design, we took initial steps toward understanding how the cockroach, Periplaneta americana, uses antenna feedback to control its orientation during a rapid wall following behav- ior. Inspired by nature's eective use of tactile feedback for rapid maneu- vering, we designed a passive, highly compliant tactile sensor for Sprawlette, a hexapedal running robot. Importantly, the same PD gains fitted to cockroach behavior also stabilize wall fol-lowing for the LLS model. Finally, we embed the template in a simulated lateral-leg-spring (LLS) model using the center of pressure as the control input. Using this system, we successfully test specific PD gains (up to a scale) fitted to the cockroach behavioral data in a "real-world" setting, lending further credence to the surprisingly simple notion that a cockroach might implement a PD controller for wall following. Furthermore, we embed the template in a robotic platform outfitted with a bio-inspired antenna. Neurophysiological ex-periments reveal that important features of the wall-following con-troller emerge at the earliest stages of sensory processing, namely in the antennal nerve. Specifically, we corroborate a prediction from a previously reported wall-following template-the simplest model that captures a behavior- that a cockroach antenna-based controller requires the rate of approach to a wall in addition to distance, e.g., in the form of a proportional-derivative (PD) controller. Our approach integrates mathematical and hardware modeling with behavioral and neurophysiological experiments. Here, we explore a system particularly well suited to exploit the synergies between biology and robotics: high-speed antenna-based wall following of the American cockroach (Periplaneta americana). The interplay between robotics and neuromechanics facilitates discoveries in both fields: nature provides roboticists with design ideas, while robotics research elucidates critical fea-tures that confer performance advantages to biological systems. ![]()
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