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

Ground robotic measurement of aeolian processes

Models of aeolian processes rely on accurate measurements of the rates of sediment transport by wind, and careful evaluation of the environmental controls of these processes. Existing field approaches typically require intensive, event-based experiments involving dense arrays of instruments. These devices are often cumbersome and logistically difficult to set up and maintain, especially near steep or vegetated dune surfaces. Significant advances in instrumentation are needed to provide the datasets that are required to validate and improve mechanistic models of aeolian sediment transport. Recent advances in robotics show great promise for assisting and amplifying scientists’ efforts to increase the spatial and temporal resolution of many environmental measurements governing sediment transport. The emergence of cheap, agile, human-scale robotic platforms endowed with increasingly sophisticated sensor and motor suites opens up the prospect of deploying programmable, reactive sensor payloads across complex terrain in the service of aeolian science. This paper surveys the need and assesses the opportunities and challenges for amassing novel, highly resolved spatiotemporal datasets for aeolian research using partially-automated ground mobility. We review the limitations of existing measurement approaches for aeolian processes, and discuss how they may be transformed by ground-based robotic platforms, using examples from our initial field experiments. We then review how the need to traverse challenging aeolian terrains and simultaneously make high-resolution measurements of critical variables requires enhanced robotic capability. Finally, we conclude with a look to the future, in which robotic platforms may operate with increasing autonomy in harsh conditions. Besides expanding the completeness of terrestrial datasets, bringing ground-based robots to the aeolian research community may lead to unexpected discoveries that generate new hypotheses to expand the science itself. For more information: Kod*lab (http://kodlab.seas.upenn.edu/

System Design, Motion Modelling and Planning for a Recon figurable Wheeled Mobile Robot

Over the past ve decades the use of mobile robotic rovers to perform in-situ scienti c investigations on the surfaces of the Moon and Mars has been tremendously in uential in shaping our understanding of these extraterrestrial environments. As robotic missions have evolved there has been a greater desire to explore more unstructured terrain. This has exposed mobility limitations with conventional rover designs such as getting stuck in soft soil or simply not being able to access rugged terrain. Increased mobility and terrain traversability are key requirements when considering designs for next generation planetary rovers. Coupled with these requirements is the need to autonomously navigate unstructured terrain by taking full advantage of increased mobility. To address these issues, a high degree-of-freedom recon gurable platform that is capable of energy intensive legged locomotion in obstacle-rich terrain as well as wheeled locomotion in benign terrain is proposed. The complexities of the planning task that considers the high degree-of-freedom state space of this platform are considerable. A variant of asymptotically optimal sampling-based planners that exploits the presence of dominant sub-spaces within a recon gurable mobile robot's kinematic structure is proposed to increase path quality and ensure platform safety. The contributions of this thesis include: the design and implementation of a highly mobile planetary analogue rover; motion modelling of the platform to enable novel locomotion modes, along with experimental validation of each of these capabilities; the sampling-based HBFMT* planner that hierarchically considers sub-spaces to better guide search of the complete state space; and experimental validation of the planner with the physical platform that demonstrates how the planner exploits the robot's capabilities to uidly transition between various physical geometric con gurations and wheeled/legged locomotion modes

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions