Robotic fish hold significant promise as efficient underwater systems, yet their inability to accurately perceive ambient flow hinders their deployment in real-world scenarios. Inspired by the natural lateral line system(LLS), a flow-responsive organ in fish that plays a crucial role in behaviors such as rheotaxis, this paper introduces the first Artificial Lateral Line System (ALLS)-based ambient flow classifier for robotic fish that allows robotic fish to perceive flow fields while swimming freely. To be specific, using just 5 pressure sensors and 3.5 minutes of swimming data, we trained a Long Short-Term Memory (LSTM) network, achieving a classification accuracy of 81.25% across 8 flow speed categories, ranging from 0.08 m/s to 0.18 m/s. A key innovation of this work is the formulation of ambient flow perception as a classification task, which not only enables the robotic fish to extract meaningful information but also enhances the robustness and generalizability of the perception framework. Extensive experiments further identify critical factors such as affecting the effectiveness of the ambient flow classifier, offering valuable insights for future development.

Robotic fish face considerable challenges in natural environment due to the absence of a comprehensive and precise model that can depict the intricate fluid-structure interactions, particularly in the presence of background flow fields. To this end, we present a novel data-driven dynamic modeling framework capable of characterizing the swimming motions of the robotic fish under various background flow conditions without the necessity for explicit flow information. The model is synthesized by an internal model with an adaptive residual acceleration model to effectively isolate and address external flow effects. Notably, the residual model employs the innovative Domain Adversarially Invariant Meta-Learning (DAIML) approach, allowing the framework to adapt to fluctuating and previously unseen background flow scenarios, enhancing its robustness and scalability. Validation through high-fidelity Computational Fluid Dynamics (CFD) simulations demonstrates the framework’s effectiveness in improving the performance of robotic fish across diverse real-world aquatic environments.

Acoustic tweezers have been a valuable tool across various fields, from nano-microfabrication to biology. Their unique characteristics enable three-dimensional particle manipulation, where acoustic trapping serves as a fundamental requirement. However, traditional methods struggle to maintain steady particle positioning due to nonlinear forces and complex dynamic coupling effects. As a result, particle oscillations are inevitable and cannot be effectively compensated by predesigned acoustic trapping. To address these challenges, this study introduces a novel visual feedback control approach that dynamically adjusts the acoustic field distribution to mitigate oscillations along the z-axis of the acoustic trapping. A binocular microscopic vision system is employed for precise particle localization, while a disturbance observer estimates the effects of strong nonlinearity and uncertainties of the acoustic trapping. The proposed methodology is validated through simulations and experiments, demonstrating a significant reduction in z-axis oscillations from 1.33× wavelength to within 0.03× wavelength. This advancement marks a step forward in achieving precise and complex acoustic manipulation using traveling-wave acoustic tweezers.

Accurate aerodynamic interaction modeling in multi-drone tasks is crucial for enhancing system stability and efficiency, especially when facing major disturbances from downwash wake effects. Conventional data-driven and empirical models mainly address simplified cases where one drone hovers or all vehicles have low absolute and relative velocities (≤ 0.5 m/s), and rely merely on relative states. In this study, we use high-fidelity Computational Fluid Dynamics (CFD) simulations to explore quadrotor interactions at higher speeds (0.5-4.0 m/s). We find that as the absolute velocities of the UAVs rise, downwash effects change significantly. To account for these discrepancies, we present a data-driven model considering both the absolute and relative properties of the downwash problem. We propose a geometric deep neural network predictor and compare its performance with existing data-driven and empirical models. Validations on two quadrotor settings show that our model gives more reliable predictions in tough scenarios and performs better in training without rigorous fine-tuning. Finally, we combine our predictor with a nonlinear feedback controller to enhance flight control under downwash disturbances. However, we encounter limitations for our speed ranges during trajectory tracking such as delays and velocity loss. Despite these challenges, our encoding and prediction method shows to be a promising step to address the downwash effects at higher speeds.We release our dataset, method, and re-implementations at: https://github.com/pavelkharitenko/flare-dw.

In the realm of digital twin systems, establishing simulation environments for creating and testing virtual agents has garnered substantial attention across various applications. The obtained control policies endow virtual agents with more realistic behaviors and interactive capabilities, finding applications in both computer animation and robotic control. While rigid-body simulators are widely used for virtual agents, achieving similar feats in fluid environments presents formidable challenges due to high complexity and exorbitant costs. One major reason is that most fluid simulators feature a fixed domain, which struggles to enable agents to freely navigate in an unbounded, obstacle-filled space, especially when computational resources are limited, thus restricting their wide utility for creating virtual agents. In this paper, we introduce a novel fluid-solid interaction simulator grounded in an efficient lattice Boltzmann solver. A key feature of this simulator is a dynamically moving local domain that encircles the agent, offering greater flexibility for obtaining control policy while maintaining efficiency in simulation. Previous methods, which anchored a square moving local domain along with the agent, suffered from severe spurious flows when the agent underwent rapid acceleration especially when the domain had to rotate, such as during a U-turn. This led to inaccurate results and instability. Conversely, we propose a novel domain-tracking method that harnesses optimal control techniques to address this issue. Our approach not only bolsters local-domain simulation stability, but also improves efficiency by employing a slender domain, which broadens the application scope of direct fluid-solid interactions for virtual agents. We validate our method by comparing simulations to physical phenomena and obtaining control policies for various virtual agents to accomplish challenging tasks. This effort culminates in a series of animations that vividly demonstrate the efficacy of the entire framework potentially used in both computer animation and robotics.

Unmanned aerial vehicles (UAVs) have demonstrated remarkable efficacy across diverse fields. Nevertheless, developing flight controllers tailored to a specific UAV design, particularly in environments with strong fluid-interactive dynamics, remains challenging. Conventional controller design experiences often fall short in such cases, rendering it infeasible to apply time-tested practices. Consequently, a simulation test bed becomes indispensable for controller design and evaluation prior to its actual implementation on the physical UAV. This platform should allow for meticulous adjustment of controllers and should be able to transfer to real-world systems without significant performance degradation. Existing simulators predominantly hinge on empirical models due to high efficiency, often overlooking the dynamic interplay between the UAV and the surrounding airflow. This makes it difficult to mimic more complex flight maneuvers, such as an abrupt midair halt inside narrow channels, in which the UAV may experience strong fluid-structure interactions. On the other hand, simulators considering the complex surrounding airflow are extremely slow and inadequate to support the design and evaluation of flight controllers. In this paper, we present a novel remedy for highly-efficient UAV flight simulations, which entails a hybrid modeling that deftly combines our novel far-field adaptive block-based fluid simulator with parametric empirical models situated near the boundary of the UAV, with the model parameters automatically calibrated. With this newly devised simulator, a broader spectrum of flight scenarios can be explored for controller design and assessment, encompassing those influenced by potent close-proximity effects, or situations where multiple UAVs operate in close quarters. The practical worth of our simulator has been authenticated through comparisons with actual UAV flight data. We further showcase its utility in designing flight controllers for fixed-wing, multi-rotor, and hybrid UAVs, and even exemplify its application when multiple UAVs are involved, underlining the unique value of our system for flight controllers.

The noncontact acoustic manipulation of particles, biosamples, droplets, and air bubbles has emerged as a promising technology in the fields of biology, chemistry, medicine, etc. The noncontact nature offers significant advantages in terms of biocompatibility, contamination free, and material versatility. However, current noncontact acoustic manipulation techniques still lack adequate selectivity, robustness, and precision controllability in complex environments. To this end, in this article, we propose an automated noncontact manipulation system that leverages a high-density ultrasonic phased transducer array in combination with a microscope to further optimize and enhance the controllability and flexibility of noncontact particle manipulation. This work presents several notable contributions. First, we successfully realized selective particle manipulation, allowing instantaneous interaction with users to perform user-designated and objective-oriented manipulation tasks. Second, we integrated a closed-loop control strategy into the system that effectively mitigates misalignment errors induced by the trapping stiffness heterogeneity of acoustic trap and enables automated precision position control of particles in complex environments (in 30-mm-wide workspace, positioning precision is 1/40 of the wavelength). Third, we proposed a reconfigurable acoustic trap design method, named pseudovortex trap, featuring real-time computing and trapping particles larger than the wavelength. The system setup, the calibration specifics, the acoustic trap design methodology, and the corresponding visual servo control scheme (in terms of selective trapping, precision positioning, and dynamic trajectory planning) are given in detail in the article. Meanwhile, the trapping stiffness and the manipulation stability are also analyzed in this work. Experimental results well demonstrated the effectiveness of the proposed system.

Training model-free intelligent agents in complex real-world scenarios using reinforcement learning (RL) often necessitates simulation-based environments due to high physical expenses. However, when simulation takes a long time, e.g., in an unsteady 3D fluid simulation with interactions to the controllable solids, existing RL algorithms meet difficulty to accomplish training within a reasonable timeframes. In this paper, we propose a novel multi-level framework for RL to accelerate convergence as the first attempt to address this difficulty. Motivated by the idea of multi-grid solver, the control policy on a virtual agent over time can be decomposed into different frequency levels, which can be progressively learned via a set of simulations in a coarse-to-fine manner. It is expected that most RL trials are performed in coarser simulations to learn lower control frequency levels with more efficient convergence, while higher frequency levels require much less RL trials, thus significantly accelerating the learning process. To implement our idea, we designed a novel multi-level residual network with a filter module attached, where each level of the network is learned by performing RL for a given simulation resolution. The proposed framework is evaluated by conducting policy learning experiments on a virtual aerial (2D) and an underwater (3D) robot, both requiring time-consuming physical simulations. Our results demonstrate a decrease in almost half in learning time compared to a direct RL approach, while achieving similar control performance.

Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynamic model that accurately describes the complex interactions between UAVs and both stationary and oscillating platforms. Integrating this model with a feedforward controller effectively compensates for GE, resulting in improved landing performance. The proposed GE model elucidates the relationship between GE and factors such as UAVs’ velocity, throttle magnitude, and the motion of the landing platform. It highlights that the GE experienced during the landing process of UAVs is not only contingent on the current state but also related to past states. The resulting GE model is parsimonious and suitable for onboard computers with limited computational power, and its accuracy has been confirmed through a series of flight experiments. To demonstrate the effectiveness of the developed UAVs landing scheme, we compared our approach with robust control and internal model control methods. Experimental results indicate that the proposed landing strategy achieves faster and smoother landings, with at least a 22% improvement in smoothness and a 25% reduction in landing time.

Dynamic model is essential for robust and reliable robotic fish motion control. Despite considerable efforts in robotic fish dynamic modeling, background flow has not been well considered yet, leading to the deterioration of applying robotic fish to practice. In this paper, we propose a novel dynamic model, termed Flow-Aware Robotic fish Model (FARM), that with well consideration to background flow using Koopman operators without increasing computation complexity. Specifically, we first collect motion data of the robotic fish in different background flow fields, and then obtain a linear approximation (the dynamic model) of nonlinear dynamics through carefully selected lifted functions. The obtained model can predict the next state based on the current state, control input, and average flow velocity of the local flow field. We evaluate the effectiveness of obtained model by comparing the Root Mean Square Error (RMSE) of predicted motion trajectories with real trajectories in various flow field environments. The results indicate that FARM is highly promising for obtaining a reliable dynamic model and can achieve comparable prediction accuracy even in unseen flow field environments with rough flow maps.

Noncontact particle manipulation (NPM) shows great application potential than its conventional counterpart particularly in terms of non-invasiveness, and thus has significantly extended robotic manipulation capacity into bio- medical engineering, material science, etc. As NPM by means of electric, magnetic, and optical field has successfully demonstrated powerful strength in both academia and industry, NPM boosted by acoustic field, however, still faces staggering challenges. It is indeed in the very recent years that controllable dynamic airborne or waterborne acoustic field modulation technology emerged in academia. In this paper, we report our latest research regarding dexterous and dynamic noncontact micro-particle manipulation on water surface effected by acoustic field in terms of automated trapping, closed-loop positioning, and real-time motion planning, which can be applied to scenarios such as parallel 3D printing, cell assembly, etc. The main contribution of this work is we demonstrated the feasibility of objective-oriented and fully automated acoustic manipulation of micro-particle in precision scale based on robotic approach in 2D plane. Experiment results showed that the repetitive positioning accuracy can reach as high as 16 μm, which is essentially the pixel scale factor.

The study of motion control for the fish-like robots in complex fluid fields is of great importance in improving the performance of underwater vehicles, due to its strong maneuverability, propulsion efficiency, and deceptive visual appearance. In this article, a novel learning-based control framework is first proposed to autonomously explore efficient control policies that are capable of performing motion control tasks in non-quiescent and unknown background flows. First, we utilize a high-fidelity simulation system, named FishGym, to generate various uniform flows. Next, a DRL-based algorithm is incorporated with the FishGym to train the fish-like robot to control its motion to optimally complete a delicately designed task (Approaching Target and Stay) in both quiescent and uniform flow. Then, the obtained control policy together with an online estimator is directly applied to a Path-Following Task. The proposed framework well balances the simulation accuracy and the computational efficiency, which is of crucial importance for effective coupling with the learning algorithm. The simulation results indicate that, via the proposed learning framework, the robot successfully acquired a swimming strategy that can be used to adapt to different background flows and tasks. Furthermore, we also observe some adaptation behavior of the robot, such as rheotaxis, that is similar to the fish in nature, which gains us more insight into the mechanism underlying the adaptation behavior of fish in a complex environment.