BACKGROUND The tight integration of sensing, actuation, and computation that biological systems exhibit to achieve shape and appearance changes (like the cuttlefish and birds in flight), adaptive load support (like the banyan tree), or tactile sensing at very high dynamic range (such as the human skin) has long served as inspiration for engineered systems. Artificial materials with such capabilities could enable airplane wings and vehicles with the ability to adapt their aerodynamic profile or camouflage in the environment, bridges and other civil structures that could detect and repair damages, or robotic skin and prosthetics with the ability to sense touch and subtle textures. The vision for such materials has been articulated repeatedly in science and fiction (“programmable matter”) and periodically has undergone a renaissance with the advent of new enabling technology such as fast digital electronics in the 1970s and microelectromechanical systems in the 1990s. ADVANCES Recent advances in manufacturing, combined with the miniaturization of electronics that has culminated in providing the power of a desktop computer of the 1990s on the head of a pin, is enabling a new class of “robotic” materials that transcend classical composite materials in functionality. Whereas state-of-the-art composites are increasingly integrating sensors and actuators at high densities, the availability of cheap and small microprocessors will allow these materials to function autonomously. Yet, this vision requires the tight integration of material science, computer science, and other related disciplines to make fundamental advances in distributed algorithms and manufacturing processes. Advances are currently being made in individual disciplines rather than system integration, which has become increasingly possible in recent years. For example, the composite materials community has made tremendous advances in composites that integrate sensing for nondestructive evaluation, and actuation (for example, for shape-changing airfoils), as well as their manufacturing. At the same time, computer science has created an entire field concerned with distributed algorithms to collect, process, and act upon vast collections of information in the field of sensor networks. Similarly, manufacturing has been revolutionized by advances in three-dimensional (3D) printing, as well as entirely new methods for creating complex structures from unfolding or stretching of patterned 2D composites. Finally, robotics and controls have made advances in controlling robots with multiple actuators, continuum dynamics, and large numbers of distributed sensors. Only a few systems have taken advantage of these advances, however, to create materials that tightly integrate sensing, actuation, computation, and communication in a way that allows them to be mass-produced cheaply and easily. OUTLOOK Robotic materials can enable smart composites that autonomously change their shape, stiffness, or physical appearance in a fully programmable way, extending the functionality of classical “smart materials.” If mass-produced economically and available as a commodity, robotic materials have the potential to add unprecedented functionality to everyday objects and surfaces, enabling a vast array of applications ranging from more efficient aircraft and vehicles, to sensorial robotics and prosthetics, to everyday objects like clothing and furniture. Realizing this vision requires not only a new level of interdisciplinary collaboration between the engineering disciplines and the sciences, but also a new model of interdisciplinary education that captures both the disciplinary breadth of robotic materials and the depth of individual disciplines.

Materials that couple sensing, actuation, computation, and communication

Fish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed boundary-lattice Boltzmann method (IB-LBM). In this framework, the fish swimming in a viscous incompressible flow is simulated with an IB-LBM which is validated by conducting two benchmark problems including a uniform flow over a stationary cylinder and a self-propelled anguilliform swimming in a quiescent flow. Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB-LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Karman gaiting. Compared to existing learning models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action spaces as well as the historical effects. This framework makes use of the high computational efficiency of the IB-LBM which is of crucial importance for the effective coupling with learning algorithms. Applications of the proposed numerical framework in point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Karman vortex street demonstrate the strategies used to adapt to different situations.

/pdf/a-numerical-study-of-fish-adaption-behaviors-in-complex-5e5xde03iw.pdf

A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary-lattice Boltzmann method.

In the body of research relevant to high-performance flapping micro air vehicles (MAV), development of light-weight, compact and energy-efficient flapping mechanisms occupies a position of primacy due to its direct impact on the flight performance and mission capability. Realization of such versatile flapping mechanism with additional ability of producing thrust levels that fulfill requirements of cruising forward flight and vertical take-off and landing (VTOL) conditions demand extensive design validation and performance evaluation. This paper presents a concerted approach for mechanism development of a 20 cm span flapping MAV through an iterative design process and synergistic fabrication options involving electrical-discharge-wire-cutting (EDWC) and injection molding. Dynamic characterization of each mechanism is done through high speed photography, power take-off measurement, wind tunnel testing and proof-of-concept test flights. The research outcome represents best-in-class mechanism for a 20 cm span...

https://journals.sagepub.com/doi/pdf/10.1260/1756-8293.7.2.181

Practical Flapping Mechanisms for 20 cm-span Micro Air Vehicles:

Flapping flight has the potential to revolutionize micro air vehicles (MAVs) due to increased aerodynamic performance, improved maneuverability, and hover capabilities. This paper presents the design of a robotic flapping wing mechanism for use in general studies involving flapping flight and laboratory-based experimental optimization of flapping trajectories. The design allows for dynamic adjustment of flapping trajectories in air or liquids with three rotational degrees of freedom on each wing. The design, instrumentation, and control of the mechanism are discussed, and experimental characterization of the mechanism's performance is presented. Preliminary trajectory optimization using a Box-Behnken design approach is used and shows successful parameter optimization. The limitations of the current mechanism are addressed. A survey of flapping mechanisms is presented.

https://journals.sagepub.com/doi/pdf/10.1260/1756-8293.4.1.31

A Differentially Driven Flapping Wing Mechanism for Force Analysis and Trajectory Optimization

We proposed an RL framework for control of flapping-wing MAVs (2006). The algorithm has been discussed and simulation results using a quasi-steady model showed initial promise. In this paper, the results from an experiment on a Drosophila-based dynamically scaled model are presented and are used to verify the control framework. Moreover, a comparison between a biological Drosophila melanogaster and the experimental results shows the actual possibility of employing the proposed approach to MAV control problem

A Reinforcement Learning Approach to Lift Generation in Flapping MAVs: Experimental Results

A dynamically scaled flapping-wing model has been developed to investigate the aerodynamic phenomena and flight performance of insect-scale flapping wings. The mechanism consists of two wings, each having 3 rotational degrees of freedom, mounted on a linear stage to permit translation in a fluid-filled tank. Each wing is equipped with a sensor for instantaneous measurement of the time-varying forces and torques. The setup permits one wing to be designated as the fore wing and the other as the hind wing so that interactions between them can be analysed to understand flight for four-winged insects. The apparatus is versatile enough to explore a range of wing morphologies as well as operational wing trajectory parameters

Force measurements on a scaled mechanical model of dragonfly in forward flight

Flapping micro aerial vehicles are interesting in applications where maneuverability is needed in confined spaces. Yet aerodynamics of insect flapping flight is not completely known and the research in this area lacks a suitable aerodynamic model that can be used for control purposes. Reinforcement learning approach is proposed which is inspired from "how" the learning is achieved in real insects in nature. The reinforcement learning controller has been simulated using a quasi-steady aerodynamic model and shown to converge to a flapping motion. The learning capacity and advantages of this approach are also discussed

A reinforcement learning approach to lift generation in flapping MAVs: simulation results

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Force measurements on a scaled mechanical model of dragonfly in forward flight

A reinforcement learning approach to lift generation in flapping MAVs: simulation results