Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting

Despite contending with constraints imposed by the environment, morphology, and physiology, animals move well by physically interactingwith the environment to use and transition between modes such as running, climbing, and self-righting. By contrast, robots struggle to do so in real world. Understanding the principles of how locomotor transitions emerge from constrained physical interaction is necessary for robots to move robustly using similar strategies. Recent studies discovered that discoid cockroaches use and transition between diverse locomotor modes to traverse beams and self-right on ground. For both systems, animals probabilistically transitioned between modes via multiple pathways, while its self-propulsion created kinetic energy fluctuation. Here, we seek mechanistic explanations for these observations by adopting a physics-based approach that integrates biological and robotic studies. We discovered that animal and robot locomotor transitions during beam obstacle traversal and ground self-righting are barrier-crossing transitions on potential energy landscapes. Whereas animals and robot traversed stiff beams by rolling their body betweenbeam, they pushed across flimsy beams, suggesting a concept of terradynamic favorability where modes with easier physical interaction are more likely to occur. Robotic beam traversal revealed that, system state either remains in a favorable mode or transitions to one when energy fluctuation is comparable to the transition barrier. Robotic self-righting transitions occurred similarly and revealed that changing system parameters lowers barriers over which comparable fluctuation can induce transitions. Thetransitionsof animalsin both systems mostly occurred similarly, but sensory feedback may facilitate its beam traversal. Finally, we developed a method to measure animal movement across large spatiotemporal scales in a terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271

Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and ground self-righting

Animals’ physical interaction with their environment, although often difficult, is effective and enables them to move robustly by using and transitioning between different modes such as running and climbing. Although robots exhibit some of these transitions, we lack a principled approach to generating and controlling them using effective physical interaction. Bridging this knowledge gap, in addition to advancing our understanding of animal locomotion, will improve robotic mobility. Recent studies of physical interaction with environment discovered that during beam obstacle traversal and ground self-righting, discoid cockroaches use and transition between diverse locomotor modes probabilistically and via multiple pathways. To traverse beams, the animal first pushes against them, but eventually pitches up due to beam restoring forces, following which it either pushes across beams (pitch mode) or rolls into the gap (roll mode). To self-right, the animal opens and pushes its wings against the ground, which pitches its body forward (metastable mode), and then rolls sideways (roll mode). Here, we seek to begin to explain these observations by integrating biological, robotic, and physics studies. We focus on pitch-to-roll and metastable-to-roll transitions of cockroaches during escape and emergency responses and feedforward-controlled robots. We discovered that across both systems, physical interaction is stochastic, with animals showing more variability. Animal and robot system states are strongly attracted to basins of their potential energy landscape, resulting in stereotyped locomotor modes. Locomotor transitions are probabilistic barrier-crossing transitions between landscape basins. Whereas the animal and robot traversed stiff beams via roll mode, they pushed across flimsy beams, suggesting that modes with easier physical interaction are more probable to occur (more favorable). Varying potential energy barriers by changing beam torsional stiffness (in the animal and robot) and kinetic energy fluctuation by changing body oscillation (in the robot) in both beam traversal and self-righting revealed that kinetic energy fluctuation comparable to the barrier facilitates probabilistic transition to the more favorable mode. Changing the system configuration (self-righting robot's wing opening) facilitates transitions by lowering the barrier. The animal's pitch-to-roll transition during beam traversal occurred even with insufficient kinetic energy fluctuation, suggesting that sensory feedback may be involved. These discoveries support the use of potential energy landscapes as a framework to understand locomotor transitions. Finally, we implemented methods for tracking and 3-D reconstruction of small animal locomotion in an existing terrain treadmill

Simplifying robotic locomotion by escaping traps via an active tail

Legged systems offer the ability to negotiate and climb heterogeneous terrains, more so than their wheeled counterparts \cite{freedberg_2012}. However, in certain complex environments, these systems are susceptible to failure conditions. These scenarios are caused by the interplay between the locomotor's kinematic state and the local terrain configuration, thus making them challenging to predict and overcome. These failures can cause catastrophic damage to the system and thus, methods to avoid such scenarios have been developed. These strategies typically take the form of environmental sensing or passive mechanical elements that adapt to the terrain. Such methods come at an increased control and mechanical design complexity for the system, often still being susceptible to imperceptible hazards. In this study, we investigated whether a tail could serve to offload this complexity by acting as a mechanism to generate new terradynamic interactions and mitigate failure via substrate contact. To do so, we developed a quadrupedal C-leg robophysical model (length and width = 27 cm, limb radius = 8 cm) capable of walking over rough terrain with an attachable actuated tail (length = 17 cm). We investigated three distinct tail strategies: static pose, periodic tapping, and load-triggered (power) tapping, while varying the angle of the tail relative to the body. We challenged the system to traverse a terrain (length = 160 cm, width = 80 cm) of randomized blocks (length and width = 10 cm, height = 0 to 12 cm) whose dimensions were scaled to the robot. Over this terrain, the robot exhibited trapping failures independent of gait pattern. Using the tail, the robot could free itself from trapping with a probability of 0 to 0.5, with the load-driven behaviors having comparable performance to low frequency periodic tapping across all tested tail angles. Along with increasing this likelihood of freeing, the robot displayed a longer survival distance over the rough terrain with these tail behaviors. In summary, we present the beginning of a framework that leverages mechanics via tail-ground interactions to offload limb control and design complexity to mitigate failure and improve legged system performance in heterogeneous environments.M.S