Forelimb position affects facultative bipedal locomotion in lizards

Escape behaviour varies with distance from safe refuge

Locomotor performance and behaviour are important for escape from predators, yet the intersection of these strategies is poorly studied. Escape behaviour is context dependent, and optimal escape theory predicts that animals that are farther from a safe refuge will generally use faster running speeds but might choose to use more variable escape paths. We studied locomotor performance and behaviour of six-lined racerunner lizards (Aspidoscelis sexlineata) escaping on natural surface runways that were varied experimentally to be either 5 or 10 m from a safe refuge. On the 5 m runway, lizards usually escaped directly towards the refuge, attained a slower maximal running speed (3.2 m s−1) at ~3 m from the start, and reached the target refuge in most of the trials (80%). On the 10 m runway, lizards used more variable behaviour, including reversals and turns, attained a faster maximal running speed (3.7 m s−1) at ~6 m from the start, and reached the final refuge in only 43% of trials. Free-ranging racerunners were rarely > 5 m from their nearest refuge and used escape paths that were typically < 5 m. Our findings align with predictions from optimal escape theory, in that the perceived risk of a predator–prey encounter can drive adjustments in locomotor behaviour and performance. Additionally, we show that the escape behaviour of free-ranging lizards closely matches their escape behaviour and performance during controlled escape trials.

An obstacle disturbance selection framework: emergent robot steady states under repeated collisions

Natural environments are often filled with obstacles and disturbances. Traditional navigation and planning approaches normally depend on finding a traversable “free space” for robots to avoid unexpected contact or collision. We hypothesize that with a better understanding of the robot–obstacle interactions, these collisions and disturbances can be exploited as opportunities to improve robot locomotion in complex environments. In this article, we propose a novel obstacle disturbance selection (ODS) framework with the aim of allowing robots to actively select disturbances to achieve environment-aided locomotion. Using an empirically characterized relationship between leg–obstacle contact position and robot trajectory deviation, we simplify the representation of the obstacle-filled physical environment to a horizontal-plane disturbance force field. We then treat each robot leg as a “disturbance force selector” for prediction of obstacle-modulated robot dynamics. Combining the two representations provides analytical insights into the effects of gaits on legged traversal in cluttered environments. We illustrate the predictive power of the ODS framework by studying the horizontal-plane dynamics of a quadrupedal robot traversing an array of evenly-spaced cylindrical obstacles with both bounding and trotting gaits. Experiments corroborate numerical simulations that reveal the emergence of a stable equilibrium orientation in the face of repeated obstacle disturbances. The ODS reduction yields closed-form analytical predictions of the equilibrium position for different robot body aspect ratios, gait patterns, and obstacle spacings. We conclude with speculative remarks bearing on the prospects for novel ODS-based gait control schemes for shaping robot navigation in perturbation-rich environments.