Publications

We propose the use of bio-inspired robotics equipped with soft sensor technologies to gain a better understanding of the mechanics and control of animal movement. Soft robotic systems can be used to generate new hypotheses and uncover fundamental principles underlying animal locomotion and sensory capabilities, which could subsequently be validated using living organisms. Physical models increasingly include lateral body movements, notably back and tail bending, which are necessary for horizontal plane undulation in model systems ranging from fish to amphibians and reptiles. We present a comparative study of the use of physical modeling in conjunction with soft robotics and integrated soft and hyperelastic sensors to monitor local pressures, enabling local feedback control, and discuss issues related to understanding the mechanics and control of undulatory locomotion. A parallel approach combining live animal data with biorobotic physical modeling promises to be beneficial for gaining a better understanding of systems in motion.

Arboreal mammals navigate a highly three dimensional and discontinuous habitat. Among arboreal mammals, squirrels demonstrate impressive agility. In a recent 'viral' YouTube video, unsuspecting squirrels were mechanically catapulted off of a track, inducing an initially uncontrolled rotation of the body. Interestingly, they skillfully stabilized themselves using tail motion, which ultimately allowed the squirrels to land successfully. Here we analyze the mechanism by which the squirrels recover from large body angular rates. We analyzed from the video that squirrels first use their tail to help stabilizing their head to visually fix a landing site. Then the tail starts to rotate to help stabilizing the body, preparing themselves for landing. To analyze further the mechanism of this tail use during mid-air, we built a multibody squirrel model and showed the righting strategy based on body inertia moment changes and active angular momentum transfer between axes. To validate the hypothesized strategy, we made a squirrel-like robot and demonstrated a fall-stabilizing experiment. Our results demonstrate squirrel's long tail, despite comprising just 3% of body mass, can inertially stabilize a rapidly rotating body. This research contributes to better understanding the importance of long tails for righting mechanisms in animals living in complex environments such as trees.

Locomotion in unstructured and irregular environments is an enduring challenge in robotics. This is particularly true at the small scale, where relative obstacle size increases, often to the point that a robot is required to climb and transition both over obstacles and between locomotion modes. In this paper, we explore the efficacy of different design features, using 'morphological intelligence', for mobile robots operating in rugged terrain, focusing on the use of active and passive tails and changes in mass distribution, as well as elastic suspensions of mass. We develop an initial prototype whegged robot with a compliant neck and test its obstacle traversal performance in rapid locomotion with varying its mass distribution. Then we examine a second iteration of the prototype with a flexible tail to explore the effect of the tail and mass distribution in ascending a slope and traversing obstacles. Based on observations from these tests, we develop a new platform with increased performance and a fin ray wheel-leg design and present experiments on traversing large obstacles, which are larger than the robot's body, of this platform with tails of varying compliance. This biorobotic platform can assist with generating and testing hypotheses in robotics-inspired biomechanics of animal locomotion.

Soft robotics can be used not only as a means of achieving novel, more lifelike forms of locomotion but also as a tool to understand complex biomechanics through the use of robotic model animals. This paper presents the control of the undulation mechanics of an entirely soft robotic subcarangiform fish, using antagonistic fast-PneuNet actuators and hyperelastic eutectic Gallium-Indium (eGaIn) embedded in silicone channels for strain sensing. To design a controller, a simple, data-driven lumped parameter approach is developed, which allows accurate but lightweight simulation, tuned using experimental data and a genetic algorithm. The model accurately predicts the robot's behavior over a range of driving frequencies and a range of pressure amplitudes, including the effect of antagonistic co-contraction of the soft actuators. An amplitude controller is prototyped using the model and deployed to the robot to reach the setpoint of a tail-beat amplitude using fully soft and real-time strain sensing.