Transient bio-inspired gliders with embodied humidity responsive actuators for environmental sensing.

Collecting temporal and spatial high-resolution environmental data can guide studies in environmental sciences to gain insights in ecological processes. The utilization of automated robotic systems to collect these types of data can maximize accuracy, resilience, and deployment rate. Furthermore, it reduces the risk to researchers deploying sensors in inaccessible environments and can significantly increase the cost-effectiveness of such studies. The introduction of transient robotic systems featuring embodied environmental sensors pushes towards building a digital ecology, while introducing only minimal disturbance to the environment. Transient robots made from fully biodegradable and non-fossil based materials, do not develop into hazardous e-waste at the end of their lifetime and can thus enable a broader adoption for environmental sensing in the real world. In this work, our approach towards the design of transient robots includes the integration of humidity-responsive materials in a glider, which is inspired by the Alsomitra macrocarpa seed. The design space of these gliders is explored and their behavior studied numerically, which allows us to make predictions on their flight characteristics. Results are validated against experiments, which show two different gliding behaviors, that can help improve the spread of the sensors. By tailoring the Cellulose-Gelatin composition of the humidity actuator, self-folding systems for selective rainwater exposure can be designed. The pH sensing layer, protected by the actuator, provides visual feedback on the pH of the rainwater. The presented methods can guide further concepts developing transient aerial robotic systems for sustainable, environmental monitoring.

Body Caudal Undulation Measured by Soft Sensors and Emulated by Soft Artificial Muscles.

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.

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry.

Due to the difficulty of manipulating muscle activation in live, freely swimming fish, a thorough examination of the body kinematics, propulsive performance, and muscle activity patterns in fish during undulatory swimming motion has not been conducted. We propose to use soft robotic model animals as experimental platforms to address biomechanics questions and acquire understanding into subcarangiform fish swimming behavior. We extend previous research on a bio-inspired soft robotic fish equipped with two pneumatic actuators and soft strain sensors to investigate swimming performance in undulation frequencies between 0.3 and 0.7 Hz and flow rates ranging from 0 to 20 c m s in a recirculating flow tank. We demonstrate the potential of eutectic gallium-indium (eGaIn) sensors to measure the lateral deflection of a robotic fish in real time, a controller that is able to keep a constant undulatory amplitude in varying flow conditions, as well as using Particle Image Velocimetry (PIV) to characterizing swimming performance across a range of flow speeds and give a qualitative measurement of thrust force exerted by the physical platform without the need of externally attached force sensors. A detailed wake structure was then analyzed with Dynamic Mode Decomposition (DMD) to highlight different wave modes present in the robot's swimming motion and provide insights into the efficiency of the robotic swimmer. In the future, we anticipate 3D-PIV with DMD serving as a global framework for comparing the performance of diverse bio-inspired swimming robots against a variety of swimming animals.