Theoretical and Experimental Investigation on the Multiple Shape Memory Ionic Polymer-Metal Composite Actuator

Development of biomimetic actuators has been an essential motivation in the study of smart materials. However, few materials are capable of controlling complex twisting and bending deformations simultaneously or separately using a dynamic control system. The ionic polymer-metal composite (IPMC) is an emerging smart material in actuation and sensing applications, such as biomimetic robotics, advanced medical devices and human affinity applications. Here, we report a Multiple Shape Memory Ionic Polymer-Metal Composite (MSM-IPMC) actuator having multiple-shape memory effect, and is able to perform complex motion by two external inputs, electrical and thermal. Prior to the development of this type of actuator, this capability only could be realized with existing actuator technologies by using multiple actuators or another robotic system. Theoretical and experimental investigation on the MSM-IPMC actuator were performed. To date, the effect of the surface electrode properties change on the actuating of IPMC have not been well studied. To address this problem, we theoretically predict and experimentally investigate the dynamic electro-mechanical response of the IPMC thin-strip actuator. A model of the IPMC actuator is proposed based on the Poisson-Nernst-Planck equations for ion transport and charge dynamics in the polymer membrane, while a physical model for the change of surface resistance of the electrodes of the IPMC due to deformation is also incorporated. By incorporating these two models, a complete, dynamic, physics-based model for IPMC actuators is presented. To verify the model, IPMC samples were prepared and experiments were conducted. The results show that the theoretical model can accurately predict the actuating performance of IPMC actuators over a range of dynamic conditions. Additionally, the charge dynamics inside the polymer during the oscillation of the IPMC are presented. It is also shown that the charge at the boundary mainly affects the induced stress of the IPMC. This study is beneficial for the comprehensive understanding of the surface electrode effect on the performance of IPMC actuators. In our study, we introduce a soft MSM-IPMC actuator having multiple degrees-of-freedom that demonstrates high maneuverability when controlled by two external inputs, electrical and thermal. These multiple inputs allow for complex motions that are routine in nature, but that would be otherwise difficult to obtain with a single actuator. To the best of our knowledge, this MSM-IPMC actuator is the first solitary actuator capable of multiple-input control and the resulting deformability and maneuverability. The shape memory properties of MSM-IPMC were theoretically and experimentally studied. We presented the multiple shape memory properties of Nafion cylinder. A physics based model of the IPMC was proposed. The free energy density theory was utilized to analyze the shape properties of the IPMC. To verify the model, IPMC samples with the Nafion as the base membrane was prepared and experiments were conducted. Simulation of the model was performed and the results were compared with the experimental data. It was successfully demonstrated that the theoretical model can well explain the shape memory properties of the IPMC. The results showed that the reheat glass transition temperature of the IPMC is lower than the programming temperature. It was also found that the back-relaxation of the IPMC decreases as the programming temperature increases. This study may be useful for the better understanding of the shape memory effect of IPMC. Furthermore, we theoretically modeled and experimentally investigated the multiple shape memory effect of MSM-IPMC. We proposed a new physical principle to explain the shape memory behavior. A theoretical model of the multiple shape memory effect of MSM-IPMC was developed. Based on our previous study on the electro-mechanical actuation effect of IPMC, we proposed a comprehensive physics-based model of MSM-IPMC which couples the actuation effect and the multiple shape memory effect. It is the first model that includes these two actuation effects and multiple shape memory effect. Simulation of the model was performed using finite element method. To verify the model, an MSM-IPMC sample was prepared. Experimental tests of MSM-IPMC were conducted. By comparing the simulation results and the experimental results, both results have a good agreement. The multiple shape memory effect and reversibility of three different polymers, namely the Nafion, Aquivion and GEFC with three different ions, which are the hydrogen, lithium and sodium, were also quantitatively tested respectively. Based on the results, it is shown that all the polymers have good multiple shape memory effect and reversibility. The ions have an influence on the broad glass transition range of the polymers. The current study is beneficial for the better understanding of the underlying physics of MSM-IPMC. A biomimetic underwater robot, that was actuated by the MSM-IPMC, was developed. The design of the robot was inspired by the pectoral fish swimming modes, such as stingrays, knifefish and cuttefish. The robot was actuated by two soft fins which were consisted of multiple IPMC samples. Through actuating the IPMCs separately, traveling wave was generated on the soft fin. Experiments were performed for the test of the robot. The deformation and the blocking force of the IPMCs on the fin were measured. A force measurement system in a flow channel was implemented. The thrust force of the robot under different frequencies and traveling wave numbers were recorded. Multiple shape memory effect was performed on the robot. The robot was capable of changing its swimming modes from Gymnotiform to Mobuliform, which has high deformability, maneuverability and agility

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments

Distributed sensing in flexible robotic fins: propulsive force prediction and underwater contact sensing

There is recent biological evidence that the pectoral fins of bluegill sunfish are innervated with nerves that respond to bending, and these fish contact obstacles with their fins. However, it is not known how fin-intrinsic sensing could be used to mediate propulsion and touch in engineered fins. The objective of this thesis is to understand the use of distributed sensing in robotic fins, inspired by bony fish fins, for the prediction of propulsive forces and for the discrimination between fluidic loading and contact loading during underwater touch. The research integrates engineering and biology and builds an understanding of fin-intrinsic sensing through study of swimming fish and robotic models of fish fins and sensors. Multiple studies identify which sensor types, sensor placement locations, and model conditions are best for predicting fin propulsive forces and for predicting the state of contact. Comparisons are made between linear and nonlinear Volterra-series convolution models to represent the mapping from sensory data to forces. Best practices for instrumentation and model selection are extracted for a broad range of swimming conditions on a complex, multi-DOF, flexible fin. This knowledge will guide the development of multi-functional systems to navigate and propel through complex, occluded, underwater environments and for sensing and responding to environmental perturbations and obstacles.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Energy Based Control System Designs for Underactuated Robot Fish Propulsion

In nature through millions of years of evolution fish and cetaceans have developed fast efficient and highly manoeuvrable methods of marine propulsion. A recent explosion in demand for sub sea robotics, for conducting tasks such as sub sea exploration and survey has left developers desiring to capture some of the novel mechanisms evolved by fish and cetaceans to increase the efficiency of speed and manoeuvrability of sub sea robots. Research has revealed that interactions with vortices and other unsteady fluid effects play a significant role in the efficiency of fish and cetaceans. However attempts to duplicate this with robotic fish have been limited by the difficulty of predicting or sensing such uncertain fluid effects. This study aims to develop a gait generation method for a robotic fish with a degree of passivity which could allow the body to dynamically interact with and potentially synchronise with vortices within the flow without the need to actually sense them. In this study this is achieved through the development of a novel energy based gait generation tactic, where the gait of the robotic fish is determined through regulation of the state energy rather than absolute state position. Rather than treating fluid interactions as undesirable disturbances and `fighting' them to maintain a rigid geometric defined gait, energy based control allows the disturbances to the system generated by vortices in the surrounding flow to contribute to the energy of the system and hence the dynamic motion. Three different energy controllers are presented within this thesis, a deadbeat energy controller equivalent to an analytically optimised model predictive controller, a $H_\infty$ disturbance rejecting controller with a novel gradient decent optimisation and finally a error feedback controller with a novel alternative error metric. The controllers were tested on a robotic fish simulation platform developed within this project. The simulation platform consisted of the solution of a series of ordinary differential equations for solid body dynamics coupled with a finite element incompressible fluid dynamic simulation of the surrounding flow. results demonstrated the effectiveness of the energy based control approach and illustrate the importance of choice of controller in performance

Improving Swimming Performance and Flow Sensing by Incorporating Passive Mechanisms

As water makes up approximately 70% of the Earth\u27s surface, humans have expanded operations into aquatic environments out of both necessity and a desire to gain potential innate benefits. This expansion into aquatic environments has consequently developed a need for cost-effective and safe underwater monitoring, surveillance, and inspection, which are missions that autonomous underwater vehicles are particularly well suited for. Current autonomous underwater vehicles vastly underperform when compared to biological swimmers, which has prompted researchers to develop robots inspired by natural swimmers. One such robot is designed, built, tested, and numerically simulated in this thesis to gain insight into the benefits of passive mechanisms and the development of reduced-order models. Using a bio-inspired robot with multiple passive tails I demonstrate herein the relationship between maneuverability and passive appendages. I found that the allowable rotation angle, relative to the main body, of the passive tails corresponds to an increase in maneuverability. Using panel method simulations I determined that the increase in maneuverability was directly related to the change in hydrodynamic moment caused by modulating the circulation sign and location of the shed vortex wake. The identification of this hydrodynamic benefit generalizes the results and applies to a wide range of robots that utilize vortex shedding through tail flapping or body undulations to produce locomotion. Passive appendages are a form of embodied control, which manipulates the fluid-robot interaction and analogously such interaction can be sensed from the dynamics of the body. Body manipulation is a direct result of pressure fluctuations inherent in the surrounding fluid flow. These pressure fluctuations are unique to specific flow conditions, which may produce distinguishable time series kinematics of the appendage. Using a bio-inspired foil tethered in a water tunnel I classified different vortex wakes with the foil\u27s kinematic data. This form of embodied feedback could be used for the development of control algorithms dedicated to obstacle avoidance, tracking, and station holding. Mathematical models of autonomous vehicles are necessary to implement advanced control algorithms such as path planning. Models that accurately and efficiently simulate the coupled fluid-body interaction in freely swimming aquatic robots are difficult to determine due, in part, to the complex nature of fluids. My colleagues and I approach this problem by relating the swimming robot to a terrestrial vehicle known as the Chaplygin sleigh. Using our novel technique we determined an analogous Chaplygin sleigh model that accurately represents the steady-state dynamics of our swimming robot. We additionally used the subsequent model for heading and velocity control in panel method simulations. This work was inspired by the similarities in constraints and velocity space limit cycles of the swimmer and the Chaplygin sleigh, which makes this technique universal enough to be extended to other bio-inspired robots

Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

The robot fish is affected by many unknown internal and external interference factors when it performs path tracking in unknown waters. It was proposed that a path tracking method based on the EFWA-ADRC (enhanced fireworks algorithmauto disturbance rejection control) to obtain high-quality tracking effect. ADRC has strong adaptability and robustness. It is an effective method to solve the control problems of nonlinearity, uncertainty, strong interference, strong coupling and large time lag. For the optimization of parameters in ADRC, the enhanced fireworks algorithm (EFWA) is used for online adjustment. It is to improve the anti-interference of the robot fish in the path tracking process. The multi-joint bionic robot fish was taken as the research object in the paper. It was established a path tracking error model in the Serret-Frenet coordinate system combining the mathematical model of robotic fish. It was focused on the forward speed and steering speed control rate. It was constructed that the EFWA-ADRC based path tracking system. Finally, the simulation and experimental results show that the control method based on EFWAADRC and conventional ADRC makes the robotic fish track the given path at 2:8s and 3:3s respectively, and the tracking error is kept within plus or minus 0:09m and 0:1m respectively. The new control method tracking steady-state error was reduces by 10% compared with the conventional ADRC. It was proved that the proposed EFWA-ADRC controller has better control effect on the controlled system, which is subject to strong interference

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

Bioinspired sensing and control for underwater pursuit

Fish in nature have several distinct advantages over traditional propeller driven underwater vehicles including maneuverability and flow sensing capabilities. Taking inspiration from biology, this work seeks to answer three questions related to bioinspired pursuit and apply the knowledge gained therein to the control of a novel, reaction-wheel driven autonomous fish robot. Which factors are most important to a successful pursuit? How might we guarantee capture with underwater pursuit? How might we track the wake of a flapping fish or vehicle? A technique called probabilistic analytical modeling (PAM) is developed and illustrated by the interactions between predator and prey fish in two case studies that draw on recent experiments. The technique provides a method for investigators to analyze kinematics time series of pursuit to determine which parameters (e.g. speed, flush distance, and escape angles) have the greatest impact on metrics such as probability of survival. Providing theoretical guarantees of capture become complicated in the case of a swimming fish or bioinspired fish robot because of the oscillatory nature fish motion. A feedback control law is shown to result in forward swimming motion in a desired direction. Analysis of this law in a pursuit scenario yields a condition stating whether capture is guaranteed provided some basic information about the motion of the prey. To address wake tracking inspiration is taken from the lateral line sensing organ in fish, which is sensitive to hydrodynamic forces in the local flow field. In experiment, an array of pressure sensors on a Joukowski foil estimates and controls flow-relative position in a Karman vortex street using potential flow theory, recursive Bayesian filtering, and trajectory-tracking, feedback control. The work in this dissertation pushes the state of the art in bioinspired underwater vehicles closer to what can be found in nature. A modeling technique provides a means to determine what is most important to pursuit when designing a vehicle, analysis of a control law shows that a robotic fish is capable of pursuit engagements with capture guarantees, and an estimation framework demonstrates how the wake of a swimming fish or obstacle in the flow can be tracked