Accurate leaf wetness detection is essential to understanding plant health and growth conditions. The mmWave radar, with its sensitivity to subtle changes, is well-suited for leaf wetness detection. Existing mmWave-based approaches utilize the Synthetic Aperture Radar (SAR) algorithm to generate image-like inputs and rely on multi-modality fusion with an RGB camera to classify leaf wetness. However, the lack of understanding of SAR-based mmWave imaging limits its accuracy in various environments. This paper presents Proteus, a novel way of understanding mmWave SAR imaging. We design a noise reduction algorithm to reduce speckle noise and improve image clarity for SAR-based mmWave imaging. Then, we incorporate phase angle data to enrich SAR texture information to capture high-resolution surface details, increasing informative features for precise wetness assessment in complex plant structures. Additionally, we introduce a cross-modality Teacher-Student network, using an RGB-based teacher model to guide the mmWave SAR-based student model for feature extraction. This network transfers the explicit knowledge in the RGB image domain to the mmWave image domain. We use commercial-off-the-shelf mmWave radar to prototype Proteus. The evaluation results show that Proteus achieves up to 96.3% accuracy across varied environmental scenarios, outperforming state-of-the-art methods.

Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.

Human-robot collaboration has been an important topic in the HRI communities. In this paper, we explore how robots can contribute to gas pipeline inspection work, and how they can support one of the most important elements of energy transportation infrastructure. To situate robots in the gas energy industry, we conducted a collaborative design study, where our co-designers were diverse stakeholders: from pipeline researchers to utility workers. The contribution of this paper is threefold: First, we explore gas pipeline work settings as a new context where robots can provide significant benefit, considering that public infrastructure is vast but understudied. Second, we collaboratively envisioned the design and use cases together with workers who are not often invited to human-robot collaboration research. Lastly, we address the importance of viewing humans in human-robot collaboration as “workers” whose roles and expertise are shaped within organizational dynamics. This study aims to shed light on the importance of a more nuanced understanding of work contexts and the positionality of robots within organizations.

This article presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states. Using higher order derivatives of general nonlinear dynamics that need not be known, we construct a Koopman-operator-based linear representation and utilize Taylor series accuracy analysis to derive an error bound. The resulting error formula is used to choose the order of derivatives in the basis functions and obtain a data-driven Koopman model using a closed-form expression that can be computed in real time. Using the inverted pendulum system, we illustrate the robustness of the error bounds given noisy measurements of unknown dynamics, where the derivatives are estimated numerically. When combined with control, the Koopman representation of the nonlinear system has marginally better performance than competing nonlinear modeling methods, such as SINDy and NARX. In addition, as a linear model, the Koopman approach lends itself readily to efficient control design tools, such as linear–quadratic regulator, whereas the other modeling approaches require nonlinear control methods. The efficacy of the approach is further demonstrated with simulation and experimental results on the control of a tail-actuated robotic fish. Experimental results show that the proposed data-driven control approach outperforms a tuned proportional–integral–derivative controller and that updating the data-driven model online significantly improves performance in the presence of unmodeled fluid disturbance. This article is complemented with a video available athttps://youtu.be/9_wx0tdDta0.

Achieving and maintaining line-of-sight (LOS) is challenging for underwater optical communication systems, especially when the underlying platforms are mobile. In this work, we propose and demonstrate an active alignment controlbased LED-communication system that uses the DC value of the communication signal as feedback for LOS maintenance. Utilizing the uni-modal nature of the dependence of the light signal strength on local angles, we propose a novel triangular exploration algorithm, that does not require the knowledge of the underlying light intensity model, to maximize the signal strength that leads to achieving and maintaining LOS. The method maintains an equilateral triangle shape in the angle space for any three consecutive exploration points, while ensuring the consistency of exploration direction with the local gradient of signal strength. The effectiveness of the approach is first evaluated in simulation by comparison with extremum-seeking control, where the proposed approach shows a significant advantage in the convergence speed. The efficacy is further demonstrated experimentally, where an underwater robot is controlled by a joystick via LED communication.

We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time. We illustrate the algorithm using a simulated multi-target search scenario.

LED-based optical communication is emerging as a low-cost, high-data-rate alternative to the traditional acoustics mode of underwater communication. However, it is challenging to establish and maintain Line-Of-Sight (LOS) between the receiver and the transmitter, especially when such systems are used by mobile robots. Hence, there is a need for an active alignment system that enables the receiver to constantly align itself towards the direction of the transmitting device. In this paper, we propose and implement an active alignment control system capable of tracking a transmitting source moving in the three-dimensional (3D) space. An extended Kalman filter is used to estimate the components of the angle between the receiver orientation and the receiver-transmitter line. Using the estimate, a proportional-integral (PI) controller is implemented to adjust the receiver orientation. The algorithm uses one measurement of the light intensity from a single photo-diode, where successive measurements are obtained via a circular scanning technique. The amplitude of the scanning is adapted to the alignment performance, to achieve a sound trade-off between estimation accuracy, signal strength, and energy consumption. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.

Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of SOAR-a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring in relatively calm waters. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload compute-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. The results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This article presents the design and implementation of Samba—an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spills and harmful algal blooms. Using the built-in camera of the smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environment. To reduce the excessive false alarms caused by the nonwater area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphones to assist image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide image segmentation, significantly reducing the energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Last, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal development of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve a 94% detection rate, a 5% false alarm rate, and a lifetime up to nearly 2 months.

Paired pectoral fins that are capable of rowing motions are an important actuation mechanism for robotic fish. Existing work in this area typically adopts a rigid connection between the actuator and the pectoral fins, which requires a faster actuation speed in the power stroke than in the recovery stroke to produce a net thrust or moment. In addition to increasing the control complexity, the latter requirement leads to slow robot speeds due to prolonged deceleration during the recovery stroke. In this paper, we propose the design of a novel flexible passive joint that connects the servomotor arm to the pectoral fin, to overcome the aforementioned problem. A dynamic model is developed for the joint and for a robotic fish equipped with such joints. The design and the model are evaluated with extensive experimental results. With symmetric actuation patterns during the power and recovery strokes, the robotic fish with the proposed joints shows clear speed advantage over the case involving rigid joints and asymmetric actuation. Motivated by the need for design optimization, the model is further utilized to investigate the influence of the joint length and stiffness on the robot locomotion performance and efficiency. It is found that, for low fin-beat frequencies, longer or more flexible joints lead to higher speeds, and the trend is reversed at high fin-beat frequencies. On the other hand, while the mechanical efficiency shows a decreasing trend when the frequency increases, it is higher with shorter joints. These findings suggest the utility of the proposed model for multiobjective design of the joint and its operating frequency.

An artificial lateral line consists of a set of flow sensors arranged around a fish-like body which aims at localizing the surrounding moving objects, a common example of which is a vibrating sphere, called a dipole. The presence of diverse sources of uncertainty in the flow environment and flow sensors leads to an error in localization and thus challenges practicability of the underlying idea, especially considering that localization accuracy significantly declines when uncertainties intensify. Accuracy of localization depends on selection of the parameters of the artificial lateral line including the number and the location of the sensors as well as the shape and the size of the lateral line. In this study, different sources of uncertainties are identified and modeled in the problem formulation. A parametric fitness function is defined that addresses computational and practical goals and encompasses the effect of different sources of uncertainties. A bi-level optimization tool is formed to find the optimum artificial lateral. Comparison of the optimized designs in different cases reveals that, the optimized design highly depends on the amount of uncertainties in the problem as well as the number of available sensors. The proposed methodology for handling noisy and nested optimization task can be extended to solve other similar problems.

Many robotic systems experience fluctuating dynamics during their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. One approach to mitigating these problems is to utilize an adaptive controller. For example, in model-free adaptive control (MFAC) a controller learns how to drive a system by continually updating link weights of an artificial neural network (ANN). However, determining the optimal control parameters for MFAC, including the structure of the underlying ANN, is a challenging process. In this paper we investigate how to enhance the online adaptability of MFAC-based systems through computational evolution. We apply the proposed methods to a simulated robotic fish propelled by a flexible caudal fin. Results demonstrate that the robot is able to effectively respond to changing fin characteristics and varying control signals when using an evolved MFAC controller. Notably, the system is able to adapt to characteristics not encountered during evolution. The proposed technique is general and can be applied to improve the adaptability of other cyber-physical systems.

Gliding robotic fish, a hybrid of underwater gliders and robotic fish, are energy-efficient and highly maneuverable, and hold strong promise for long-duration sampling of underwater environments. In this paper a novel systematic autonomous water-column-based sampling scheme for gliding robotic fish is proposed to measure the three-dimensional spatial distributions of variables of interest in aquatic environments. The scheme exploits energy-efficient spiral-down motion to sample each water column, followed by sagittal-plane glide-up towards the direction of next water column. Once surfacing, the robot uses GPS guidance to reach the next column location through swimming. To enhance the path tracking performance, a two-degree-of-freedom controller involving H∞control is used in the spiral motion, and a sliding-mode controller is employed to regulate the yaw angle during glide-up. The sampling scheme has been implemented on a gliding robotic fish prototype, “Grace”, and verified first in pool experiments, and then in field experiments involving the sampling of harmful algae concentration in the Wintergreen Lake, Michigan.

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This paper presents the design and implementation of Samba -- an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spill and harmful algal blooms. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environments. To reduce the excessive false alarms caused by the non-water area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from the continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide the image segmentation, significantly reducing energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Lastly, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal evolution of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve 94% detection rate, 5% false alarm rate, and a lifetime up to nearly two months.

Averaging of robotic fish dynamics is of interest for the purposes of path planning and controller design due to the rhythmic movement of the robot. For a faithful dynamic model of robotic fish, however, classical averaging or geometric averaging typically cannot produce an average model that is accurate and in the meantime amenable to analysis or control design. In this paper, a novel averaging approach is proposed for tail-actuated robotic fish dynamics, in which the tail-generated hydrodynamic force is modeled with the Lighthill's large-amplitude elongated-body theory. The approach consists of scaling the force and moment terms and, then, conducting classical averaging. Numerical investigation reveals that the scaling function for the force term is a constant independent of tail-beat patterns, while the scaling function for the moment term depends linearly on the tail-beat bias. Extensive simulation and experimental results, comparing the predictions from the original and average models, are presented to support the effectiveness of the averaging approach. Existence and local stability of the equilibria for the average model are further analyzed. These equilibria are subsequently used to obtain an analytical solution for steady turning parameters, such as turning period and turning radius, without running simulation of the original or average dynamic models.

Pectoral fins are important actuation mechanisms in achieving maneuvering and propulsion for robotic fish. Existing designs predominantly adopt a rigid joint connecting the actuator to the pectoral fin, which requires differential actuation speeds in the power and recovery strokes in order to produce thrust and thus limits the overall actuation frequency. To address this problem, in this paper we propose a novel design of a flexible joint, which enables the pectoral fin to sweep back passively along the fish body in the recovery stroke, to minimize the drag, while maintaining the prescribed motion in the power stroke. A dynamic model is presented for a robotic fish propelled by pectoral fins incorporating such flexible joints, where the pectoral fin mechanism is modeled by two rigid segments connected with a pair of torsional spring and damper. This design results in a net thrust even with the same recovery and power stroke speed within each fin beat cycle, which simplifies the fin control. Experimental results on a robotic fish prototype are presented to validate the effectiveness of the proposed model, and to demonstrate the significant advantage of the proposed fin joint over the rigid joint. Furthermore, modeling analysis and experimental investigation are used to examine the influence of the joint length and stiffness on the locomotion performance.

Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to the limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of GPS localization errors, robotic fish movement, and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our profiling algorithms on mote-class robotic sensor platforms.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This article presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power mobile sensing platforms called robotic fish . Robotic fish networks are a typical example of cyber-physical systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Designing a robotic fish is a challenging endeavor due to the non-linear dynamics of underwater environments. In this paper, we present an evolutionary computation approach for designing the caudal fin of a carangiform robotic fish. Evolutionary experiments are performed in a simulated environment utilizing a mathematical model to approximate the hydrodynamic motion of a flexible caudal fin. With this model, time-consuming computational fluid dynamic simulations can be avoided while maintaining a physically realistic simulation. Two approaches are employed to maximize a robotic fish's average velocity. First, a hill-climbing algorithm is applied to find the optimal stiffness for a fixed shape caudal fin. Next, both fin stiffness and shape are simultaneously optimized with a genetic algorithm. Additionally, simulated caudal fins are compared to physically validated fins, which were fabricated with the aid of a 3D printer and tested on a robotic fish prototype. Results show a correlation between evolved results, model predicted behavior, and physical robot performance with some disparity due to the difficulty in accurately approximating real world performance in a simulation environment. Despite the disparity, evolutionary design is shown to be a viable process.

The concept of gliding robotic fish combines gliding and fin-actuation mechanisms to realize energy-efficient locomotion and high maneuverability, and holds strong promise for mobile sensing in versatile aquatic environments. In this paper we present the modeling and design of a miniature fish-like glider, a key enabling component for gliding robotic fish. The full dynamics of the glider is first derived and then reduced to the sagittal plane, where the lift, drag, and pitch moment coefficients are obtained as linear or quadratic functions of the attack angle based on computational fluid dynamics (CFD) analysis. The model is used to design the glider by accommodating stringent constraints on dimensions yet meeting the desired specification on speed. A fully untethered prototype of underwater glider is developed, with a weight of 4 kg and length of 40 cm. With a net buoyancy of 20 g, it realizes a steady gliding speed of 20 cm/s. The volume and net buoyancy of this glider are less than 10% and 5%, respectively, of those of reported gliders in the literature, and its speed per unit net buoyancy is over 9 times of those other vehicles. Experimental results have shown that the model is able to capture well both the steady glide behavior under different control inputs, and the dynamics during transients.

Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class robotic sensor platforms.

A gliding robotic fish is developed for promising applications in aquatic environment monitoring. The design concept combines the strengths of both underwater gliders and robotic fish, featuring long operation duration and high maneuverability. This paper presents both analytical and experimental results for the three-dimension spiraling motion, an essential working pattern of the gliding fish for detecting pollution in a water column. A dynamic model of the gliding robotic fish with actuated tail is established. Then the steady-state spiraling equations are derived and solved recursively using Newton's method. The gliding fish prototype is tested in experiments. Both model prediction and experimental results show that the spiraling motion has very low energy consumption, and the gliding fish can achieve high maneuverability with a turning radius less than 1 m, 2.5% of the reported turning radius of a typical underwater glider.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This paper presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power, mobile sensing platforms called robotic fish. Robotic fish networks are a typical example of Cyber-Physical Systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

In this paper, we present a new dynamic model for a carangiform robotic fish by merging rigid-body dynamics with Lighthill's large-amplitude elongated-body theory. In addition, we refine the model by introducing an effective approach to the evaluation of time-varying drag force and moment coefficients. It is known that these coefficients vary with the angle of attack, but the angle of attack oscillates constantly for a carangiform robotic fish because of the recoil motion of the body. By examining the individual effects of tail beat bias, frequency, amplitude on the attack angle, we find that the bias has much more dominant impact than the other two factors. Based on this observation, we develop a convenient scheme for determining the drag force and moment coefficients, as a function of the tail beat bias. Experimental results on a robotic fish prototype propelled with a servo-controlled rigid tail have verified the proposed model. In particular, we show that the pressure force at the tail tip, which is often ignored in related literature, plays a significant role in the dynamics. The effectiveness of the proposed scheme for adapting the drag force and moment coefficients is also demonstrated through the comparison with a model that uses constant drag coefficients.

Turning is one of the most important maneuvers for biological and robotic fish. In our group's prior work, an analytical framework was proposed for modeling the steady turning of fish, given asymmetric, periodic body/tail movement or deformation. However, the approach was not illustrated with simulation or validated with experiments. The contributions of the current paper are three fold. First, an extension to the modeling framework is made with a more rigorous formulation of the force balance equation. Second, we have worked out two examples explicitly, one with an oscillating, rigid tail, and the other with a flexible tail having a uniform curvature, and compared their turning behaviors through numerical results. Third, for model validation purposes, a robotic fish prototype has been developed, with the tail shaft controlled precisely by a servo motor. For a rigid tail, experimental results have confirmed the model prediction that, for the tested range, the steady-state turning radius and turning period decrease with an increasing bias in the tail motion, and that the turning period drops with an increasing tail beat frequency. We have also found that, with a flexible fin attached to the tail shaft, the robot can achieve faster turning with a smaller radius than the case of a rigid fin, and modeling within the same framework is underway to understand this phenomenon.

In this paper we present a novel framework for computing the steady turning motion of a robotic fish undergoing periodic body and/or tail deformation. Taking the turning radius and the angular velocity as unknowns, we obtain the absolute motion trajectories of points on the ldquospinal columnrdquo of robotic fish by superimposing relative body/tail motions on the rigid body circular motion. The hydrodynamic reactive force and the resulting moment are then computed from the motion trajectories, using Lighthill's large-amplitude elongated-body theory, in terms of the two turning parameters. By integrating the dynamics of rigid body motion and averaging out oscillations, implicit equations involving the turning parameters can be established and solved. We also discuss the plan of applying the proposed framework to the modeling of steady turning maneuvers of biomimetic robotic propelled by an ionic polymer-metal composite (IPMC) caudal fin.

Soft actuation materials, such as Ionic Polymer-Metal Composites (IPMCs), are gaining increasing interest in robotic applications since they lead to compact and biomimetic designs. In this paper, we propose the use of soft actuation materials as active joints for propelling biomimetic robotic fish. An analytical model is developed to compute the thrust force generated by a two-link tail and the resulting moments in the active joints. The computed joint moments can be combined with internal dynamics of actuation materials to provide realistic kinematic constraints for the joints. Computational fluid dynamics (CFD) modeling is also adopted to examine the flow field, the produced thrust, and the bending moments in joints for the two-link tail. Good agreement is achieved between the analytical modeling and the CFD modeling, which points to a promising two-tier framework for the understanding and optimization of robotic fish with a multi-link tail. We also show that, comparing to a one-link bending tail, a two-link tail is able to produce much higher thrust and more versatile maneuvers, such as backward swimming.

Reported conjugated polymer actuators have typically been limited to bender or linear extender configurations. In this paper, we present a fiber-reinforced conjugated polymer actuator capable of torsional motion. By incorporating platinum fibers into the material matrix during the electrochemical fabrication process, we create anisotropy in the interaction between the fiber and the material matrix, resulting in torsion and other associated deformations upon actuation. A nonlinear elasticity-based model is utilized to capture the actuator performance for both small and large deformations. The effectiveness of the model is verified through comparison with experimental results.

Fine-grained (sub-meter) ranging and localization is critical to the deployment of dense, mobile sensor networks in aquatic environments. However, such a task is faced with a number of challenges, including noisy underwater environments, limitation on size and complexity of localization hardware, and constraints on computing capabilities of sensor platforms. In this paper we present a sliding-window discrete Fourier transform (DFT)-based algorithm for precise detection of the arrival of a monotone acoustic signal, as a key enabling step in measuring the time of flight (TOF) of the acoustic signal for localization of the sensor node. The algorithm accommodates the rise dynamics of the signal and compensates for the latency in detection given the signal model, detection threshold, and steady-state signal amplitude. The algorithm is implemented onboard a small biomimetic robotic fish, and experiments in an indoor pool have shown that the proposed method results in an underwater ranging error of 1.4 wavelengths (74.3 cm), and is thus promising for localization of dense aquatic networks. The proposed method has also shown robustness in comparison with other tested methods including a matched filter-type method.

In this paper a physics-based model is proposed for a biomimetic robotic fish propelled by an ionic polymermetal composite (IPMC) actuator. Inspired by biological fins, a passive plastic foil is attached to the IPMC beam. The model incorporates both IPMC actuation dynamics and the hydrodynamics, and captures the interactions between the IPMC actuator and the plastic foil. Experimental results have shown that the proposed model is able to predict the steady-state cruising speed of the robotic fish under a periodic actuation input. Since the majority of the model parameters are expressed in terms of fundamental physical properties and geometric dimensions, the model is expected to be instrumental in optimal design of the robotic fish.

In this paper a dynamic contact model is presented based on the Johnson-Kendall-Roberts (JKR) theory. The classical JKR model captures the contact properties for the quasi-static condition, which does not hold in some applications related to micromanipulation. The proposed model includes rate-dependent terms and accounts for dynamic effects such as damping. The model predicts that the threshold of the force for separating two objects in contact depends on the slew rate of the force. The implication of this result in vibrational releasing of micro objects is explored with the goal of minimizing the uncertainty in positions of released objects. Simulation results are reported on releasing of a micro sphere from a cantilever manipulator actuated by a piezoelectric layer