Biomimetic Robot Research Articles

Measurement Methods for Droplet Adhesion Characteristics and Micrometer-Scale Quantification of Contact Angle on Superhydrophobic Surfaces: Challenges and Opportunities.

Inspired by nature, superhydrophobic surfaces have been widely studied. Usually the wettability of a superhydrophobic surface is quantified by the macroscopic contact angle. However, this method has various limitations, especially for precision micro devices with superhydrophobic surfaces, such as biomimetic artificial compound eyes and biomimetic water strider robots. These precision micro devices with superhydrophobic surfaces proposed a higher demand for the quantification of contact angles, requiring contact angle quantification technology to have micrometer-scale measurement capabilities. In this review, it is proposed to achieve micrometer-scale quantification of superhydrophobic surface contact angles through droplet adhesion characteristics (adhesion force and contact radius). Existing contact angle quantification techniques and droplet characteristics' measurement methods were described in detail. The advancement of micrometer-scale quantification technology for the contact angle of superhydrophobic surfaces will enhance our understanding of superhydrophobic surfaces.

Design and architecture of a slender and flexible underwater robot

This paper presents the design and analysis of a biomimetic underwater snake-like robot, addressing the main limitations of current underwater robotic systems in terms of maneuverability and adaptability in complex environments. The innovative design incorporates flexible joint modules that significantly enhance the robot’s ability to navigate through narrow and irregular terrains, which is a notable limitation in traditional rigidly connected underwater robots. These flexible joints provide increased degrees of freedom and enable the robot to absorb and release energy, ensuring stability even under external impacts, thus extending the operational lifespan of the robot. Finite element analysis demonstrates the flexible joints’ superior performance in various underwater conditions, offering a greater range of motion and workspace compared to rigid connections. The results indicate that the robot’s modular design, combined with the flexible joint module, leads to improved agility and maneuverability, allowing for precise and intentional operation. The control module, equipped with advanced sensors and a CPU, manages the complex dynamics introduced by the flexible joints, ensuring effective navigation and operation. The specific advantages of this design include the robot’s enhanced structural integrity, its ability to conform to irregular surfaces, and its adaptability to environmental variations. The paper concludes with a discussion on the implications of these findings for the future design and operation of underwater serpentine robots, emphasizing the need for a balance between the effects of elastic modulus and workspace to maximize the benefits of flexible joints.

Learning control for body caudal undulation with soft sensory feedback

Soft bio-mimetic robotics is a growing field of research that seeks to close the gap with animal robustness and adaptability where conventional robots fall short. The embedding of sensors with the capability to discriminate between different body deformation modes is a key technological challenge in soft robotics to enhance robot control–a difficult task for this type of systems with high degrees of freedom. The recently conceived Linear Repetitive Learning Estimation Scheme (LRLES)–to be included in the traditional Proportional–integral–derivative (PID) control–is proposed here as a way to compensate for uncertain dynamics on a soft swimming robot, which is actuated with soft pneumatic actuators and equipped with soft sensors providing proprioceptive information pertaining to lateral body caudal bending akin to a goniometer. The proposed controller is derived in detail and experimentally validated, with the experiment consisting of tracking a desired trajectory for the bending angle envelope while continuously oscillating with a constant frequency. The results are compared vis a vis those achieved with the traditional PID controller, finding that the PID endowed with the LRLES outperforms the PID controller (though the latter has been separately tuned) and experimentally validating the novel controller’s effectiveness, accuracy, and matching speed.

An Underwater Biomimetic Robot that can Swim, Bipedal Walk and Grasp

In developing and exploring extreme and harsh underwater environments, underwater robots can effectively replace humans to complete tasks. To meet the requirements of underwater flexible motion and comprehensive subsea operation, a novel octopus-inspired robot with eight soft limbs was designed and developed. This robot possesses the capabilities of underwater bipedal walking, multi-arm swimming, and grasping objects. To closely interact with the underwater seabed environment and minimize disturbance, the robot employs a cable-driven flexible arm for its walking in underwater floor through a bipedal walking mode. The multi-arm swimming offers a means of three-dimensional spatial movement, allowing the robot to swiftly explore and navigate over large areas, thereby enhancing its flexibility. Furthermore, the robot’s walking arm enables it to grasp and transport objects underwater, thereby enhancing its practicality in underwater environments. A simplified motion models and gait generation strategies were proposed for two modes of robot locomotion: swimming and walking, inspired by the movement characteristics of octopus-inspired multi-arm swimming and bipedal walking. Through experimental verification, the robot’s average speed of underwater bipedal walking reaches 7.26 cm/s, while the horizontal movement speed for multi-arm swimming is 8.6 cm/s.

Pyroelectric-Accelerated Perovskite Photodetector for Picosecond Light Detection and Ranging.

Light detection and ranging (LiDAR) is indispensable in applications such as unmanned aerial vehicles, autonomous driving, and biomimetic robots. However, the precision and available distance of LiDAR are constrained by the speed and sensitivity of the photodetector, necessitating the use of expensive and energy-consuming avalanche diodes. To address these challenges, in this study, a pyroelectricity-based acceleration strategy with 2D-(graded 3D) perovskite heterojunction is proposed to achieve a record high speed (27.7 ns with an active area of 9 mm2, and 176 ps with an active area of 0.2 mm2) and high responsivity (0.65 A W-1) at zero bias. This success is attributed to the unique mechanism where the electrons from the pyroelectric effect at the Cl-rich 2D/3D interface directly recombine with excess holes during light-dark transitions, breaking speed limitations related to carrier mobility and capacitive effect. Furthermore, the introduced pyroelectric effect significantly enhances the photoresponse, resulting in a self-powered external quantum efficiency exceeding 100%. The study also demonstrates precise position detection at the centimeter level. In conclusion, this research presents a pioneering approach for developing high-speed photodiodes with exceptional sensitivity, mitigating energy and cost concerns in LiDAR applications.

Neuro-Robotics: Bridging Neuroscience and Robotics

The field of neurorobotics represents the combination of neuroscience and robotics, aiming to elucidate neural functional principles and use them to create intelligent robots. This article considers the symbiotic relationship between the two fields and explores how insights from neuroscience can inform the design and control of robots; Robotic platforms offer a unique opportunity to learn and validate insights from neuroscience. For example, this article focuses on the core concepts of neuroscience and robotics and highlights key advances that support the integration of these fields, including brain-computer interfaces, neurorobotic simulations, and bionic design. It examines how discoveries in neuroscience, such as the understanding of sensorimotor control, learning processes, and cognitive processes, are supporting the creation of biomimetic robots that can address behavioural challenges and interact with their environments.

Untethered multifunctional biomimetic soft actuator with programmable shape deformation capabilities and localized maneuverability

Stimulus-responsive materials are driving the rapid developments of soft actuators. Despite exciting advancements having been made, the integration of multistimulus response capability, programmable shape deformation, and local manipulability into a single material system remains a challenge. In this study, we developed an untethered bilayer material that is responsive to both light and magnetic fields. This bilayer material is achieved by integrating a photosensitive bilayer material with magnetoactive soft materials with programmable magnetic domains. The resulting actuators exhibit excellent responsiveness to both optical and magnetic stimuli. In addition, they can be manipulated through the synergistic effects of local light stimulation and a magnetic field, thereby enhancing their control versatility. These capabilities make the device suitable for a wide range of applications, including biomimetic robots and flexible electronic devices. The underlying principle of coupling shape-morphing and responsive actuation offers significant promise for future advancements to the development of soft robots and other functional devices.

Bioinspired Flexible Capacitive Sensor for Robot Positioning in Unstructured Environments.

The evolution of bionic machines into intelligent robots to adapt to real scenarios is inseparable from positioning sensors. However, traditional positioning methods such as camera arrays, ultrasound, or GPS are limited in narrow concealed spaces, harsh temperatures, or dynamic light fields, which hinder the practical application of special robots. Here, we report a flexible sensor inspired by Gnathonemus petersii that enables robots to achieve contactless and high-precision spatial localization independent of the unstructured features of the environment. Sensors are obtained from low-cost materials (carbon nanotubes and polyimides) and simple structures (fibers) and preparation processes (spin-coating). Experiments and simulations confirmed the high resolution (<1 mm) of the sensor over a large distance detection range (>150 mm) and high bandwidth (0-520 MPa) of contact force. Moreover, the sensing capability is still feasible when the sensor is bent to various curvatures and not affected under harsh conditions such as ultralow temperatures (below -78 °C), ultrahigh temperatures (over 250 °C), darkness, or brightness. We demonstrate the practical potential of the proposed sensors for a biomimetic hyper-redundant continuum robot to locate and avoid collisions in unstructured environments.

Structure design and coordinated motion analysis of bionic crocodile robot

Crocodiles, one of the oldest and most resilient species on Earth, have demonstrated remarkable locomotor abilities both on land and in water, evolving over millennia to adapt to diverse environments. In this study, we draw inspiration from crocodiles and design a highly biomimetic crocodile robot equipped with multiple degrees of freedom and articulated trunk joints. This design is based on comprehensive analysis of the structural and motion characteristics of real crocodiles. The bionic crocodile robot has a problem of limb-torso incoordination during movement. To solve this problem, we used the D-H method for both forward and inverse kinematics analysis of the robot’s legs and spine. Through a series of simulation experiments, we investigated the robot’s motion stability, fault tolerance, and adaptability to environments in two motor patterns: with and without spine and tail movements. The experimental results show that the bionic crocodile robot exhibits superior motion performance when the spine and tail cooperate with the extremities. This study not only demonstrates the potential of biomimicry in robotics but also underscores the significance of understanding how nature’s designs can inform and enhance technological innovations.

Lightweight Underwater Object Detection Algorithm for Embedded Deployment Using Higher-Order Information and Image Enhancement

Underwater object detection is crucial in marine exploration, presenting a challenging problem in computer vision due to factors like light attenuation, scattering, and background interference. Existing underwater object detection models face challenges such as low robustness, extensive computation of model parameters, and a high false detection rate. To address these challenges, this paper proposes a lightweight underwater object detection method integrating deep learning and image enhancement. Firstly, FUnIE-GAN is employed to perform data enhancement to restore the authentic colors of underwater images, and subsequently, the restored images are fed into an enhanced object detection network named YOLOv7-GN proposed in this paper. Secondly, a lightweight higher-order attention layer aggregation network (ACC3-ELAN) is designed to improve the fusion perception of higher-order features in the backbone network. Moreover, the head network is enhanced by leveraging the interaction of multi-scale higher-order information, additionally fusing higher-order semantic information from features at different scales. To further streamline the entire network, we also introduce the AC-ELAN-t module, which is derived from pruning based on ACC3-ELAN. Finally, the algorithm undergoes practical testing on a biomimetic sea flatworm underwater robot. The experimental results on the DUO dataset show that our proposed method improves the performance of object detection in underwater environments. It provides a valuable reference for realizing object detection in underwater embedded devices with great practical potential.

Biomimetic lizard robot for adapting to Martian surface terrain

The exploration of the planet Mars still is a top priority in planetary science. The Mars surface is extensively covered with soil-like material. Current wheeled rovers on Mars have been occasionally experiencing immobilization instances in unexpectedly weak terrains. The development of Mars rovers adaptable to these terrains is instrumental in improving exploration efficiency. Inspired by locomotion of the desert lizard, this paper illustrates a biomimetic quadruped robot with structures of flexible active spine and toes. By accounting for spine lateral flexion and its coordination with four leg movements, three gaits of tripod, trot and turning are designed. The motions corresponding to the three gaits are conceptually and numerically analyzed. On the granular terrains analog to Martian surface, the gasping forces by the active toes are estimated. Then traversing tests for the robot to move on Martian soil surface analog with the three gaits were investigated. Moreover, the traversing characteristics for Martian rocky and slope surface analog are analyzed. Results show that the robot can traverse Martian soil surface analog with maximum forward speed 28.13 m s−1 turning speed 1.94° s−1 and obstacle height 74.85 mm. The maximum angle for climbing Martian soil slope analog is 28°, corresponding slippery rate 76.8%. It is predicted that this robot can adapt to Martian granular rough terrain with gentle slopes.

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators.

Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

Using a biomimetic sonar bat robot to explore the sensory world of bats in the field and in the laboratory

Bat species such as those belonging to the families of horseshoe and Old World leaf-nosed bats make their navigation decisions based on echoes that are often received from dense vegetation. To gain an understanding of how these animals accomplish navigation based on such complicated clutter echoes, a biomimetic sonar system that replicates the auditory periphery of bats has been used as synthetic observer to probe the sensory stimulus ensemble of the bats in a flight tunnel as well as in the field. Both laboratory and field experiments were carried out in Brunei on the island of Borneo. For the flight tunnel, the biomimetic sonar robot has been used to record a large number of pulse and echo pairs in conjunction with an array consisting of 28 ultrasonic microphones. In this experiment, the sonar head was positioned at points spaced on a 3d grid that covers the tunnel's volume. This data set will be used to train a deep neural network to predict the inputs to the ears of a bat maneuvering in the tunnel. In the field experiments, the sonar head was carried off road in various tropical-forest habitats to record echo samples in conjunction with precise GPS location references.

Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning

Aquatic organisms are known for their ability to generate efficient propulsion with low energy expenditure. While existing research has sought to leverage bio-inspired structures to reduce energy costs in underwater robotics, the crucial role of control policies in enhancing efficiency has often been overlooked. In this study, we optimize the motion of a bio-mimetic robotic fish using deep reinforcement learning (DRL) to maximize propulsion efficiency and minimize energy consumption. Our novel DRL approach incorporates extended pressure perception, a transformer model processing sequences of observations, and a policy transfer scheme. Notably, significantly improved training stability and speed within our approach allow for end-to-end training of the robotic fish. This enables agiler responses to hydrodynamic environments and possesses greater optimization potential compared to pre-defined motion pattern controls. Our experiments are conducted on a serially connected rigid robotic fish in a free stream with a Reynolds number of 6000 using computational fluid dynamics simulations. The DRL-trained policies yield impressive results, demonstrating both high efficiency and propulsion. The policies also showcase the agent's embodiment, skillfully utilizing its body structure and engaging with surrounding fluid dynamics, as revealed through flow analysis. This study provides valuable insights into the bio-mimetic underwater robots optimization through DRL training, capitalizing on their structural advantages, and ultimately contributing to more efficient underwater propulsion systems.

A Quad‐Unit Dielectric Elastomer Actuator for Programmable Two‐Dimensional Trajectories

Resonant actuation and multi‐degree‐of‐freedom (DoF) actuation can optimize the output matrices and greatly expand the versatility of dielectric elastomer actuators (DEAs) for practical applications. However, developing a multi‐DoF resonant DEA that benefits both from the maximized stroke outputs in resonant actuation and the versatility in multi‐DoF actuation remains a challenging task due to the slow responses of the widely adopted acrylic elastomers and the exponentially scaled complexity in the nonlinear dynamics. Herein, a multi‐DoF resonant DEA is reported by adopting four radially organized planar DE units fabricated by low‐loss silicone elastomers to output versatile programmable two‐dimensional quasi‐static and resonant trajectories. The rich nonlinear dynamics of the proposed quad‐unit DEA (QUDEA) are characterized and the critical parameters for two‐dimensional trajectory programming are uncovered through extensive modeling and experimental studies. To showcase the two‐dimensional trajectory programming capabilities of the QUDEA, demonstrations of Chinese characters writing and hummingbird flapping patterns mimicking using the proposed QUDEA are developed. The proposed QUDEA is expected to have potential in various applications such as in experimental biology, biomimetic robotics, and industrial manufacturing devices.

A numerical study on the hydrodynamics of a swimming crocodile model

Aiming to uncover the propulsion mechanisms underlying a cruising crocodile, we conduct computational fluid dynamics (CFD) simulations on the flow around a simplified three-dimensional model of the Crocodylus siamensis. The locomotion of the crocodile model is realized through undulating its body and tail, mimicking a crocodile-like swimming pattern. At a cruising speed of U∞ = 0.5 m/s (corresponding to a Reynolds number Re = 9.95 × 105 based on U∞ and the body length L), the hydrodynamics of the crocodile model are investigated, taking into account effects of the undulation parameters (i.e., amplitude A and frequency f). The normalized undulation parameters cover broad ranges of 0.6 ≤ A* = A/W ≤ 1.0 and 0.25 ≤ f * = fW/U∞ ≤ 0.625, where W is the body width. The CFD simulations are conducted in ANSYS Fluent, with the SST k–ω turbulence model and user-defined functions for dynamic mesh being used. Numerical results reveal that A* and f * render profound effects on the hydrodynamic performance of the crocodile model. The time-mean axial force coefficient (CA¯) and power coefficient (C¯Power) exhibit rapid growth with increasing A* and/or f *, while the root mean square lateral force coefficient (Cy,rms) is more dependent on f * than on A*. It is further found that, irrespective of A*, CA¯ and C¯Power can be well scaled with Strouhal number St (= 2fA/U∞) or St2(1 − U∞/c). Furthermore, distinct flow patterns are observed in the wake of the crocodile model undulating at different St, corresponding to the drag, transition (or cruising), and thrust type swimming, respectively. Discussion is made on the wake flow structures and their connections to the generation of the hydrodynamic forces. The findings from this work contribute to the understanding of the propulsion mechanisms of the swimming crocodile, meaningful for the design of efficient biomimetic amphibious robots.

Of bats and robots

Some bat species feature joint adaptations to their biosonar sensing and flight systems that allow them to navigate and hunt in dense vegetation. Understanding how biosonar sensing and flight are connected in these species poses a challenge: Kinematics recordings of bat-flight can document the system outputs, but due to various limitations, especially on processing, analysis of bat flight has been limited to minimal numbers of flights and hence has had difficulty to capture the natural variability in flight maneuvers. On the input side of the bats' flight-control system, it is necessary to understand the stimulus ensemble that guides flight in dense vegetation. Navigation in these cases must be based on clutter echoes, i.e., signals consisting of many unresolved components. Since the biosonar inputs into a bat are difficult to record without heavy interference with the animals' behaviors. Hence, biomimetic sonar robots can be used to collect stimuli from natural environments and their recreations. With their superior abilities to find patterns, deep-learning offer an opportunity to cut through the complexity of the input and output data of bat flight control and elucidate how clutter echoes are interfaced with the high-dimensional flight kinematics of bats to create the animals' exceptional maneuvering capabilities.

Leveraging Imitation Learning on Pose Regulation Problem of a Robotic Fish.

In this article, the pose regulation control problem of a robotic fish is investigated by formulating it as a Markov decision process (MDP). Such a typical task that requires the robot to arrive at the desired position with the desired orientation remains a challenge, since two objectives (position and orientation) may be conflicted during optimization. To handle the challenge, we adopt the sparse reward scheme, i.e., the robot will be rewarded if and only if it completes the pose regulation task. Although deep reinforcement learning (DRL) can achieve such an MDP with sparse rewards, the absence of immediate reward hinders the robot from efficient learning. To this end, we propose a novel imitation learning (IL) method that learns DRL-based policies from demonstrations with inverse reward shaping to overcome the challenge raised by extremely sparse rewards. Moreover, we design a demonstrator to generate various trajectory demonstrations based on one simple example from a nonexpert helper, which greatly reduces the time consumption of collecting robot samples. The simulation results evaluate the effectiveness of our proposed demonstrator and the state-of-the-art (SOTA) performance of our proposed IL method. Furthermore, we deploy the trained IL policy on a physical robotic fish to perform pose regulation in a swimming tank without/with external disturbances. The experimental results verify the effectiveness and robustness of our proposed methods in real world. Therefore, we believe this article is a step forward in the field of biomimetic underwater robot learning.

Anatomically-Inspired Robotic Finger with SMA Tendon Actuation for Enhanced Biomimetic Functionality.

This research introduces an advanced robotic finger designed for future generalist robots, closely mimicking the natural structure of the human finger. The incorporation of rarely discussed anatomical structures, including tendon sheath, ligaments, and palmar plates, combined with the usage of anatomically proven 3D models of the finger, give rise to the highly accurate replication of human-like soft mechanical fingers. Benefiting from the accurate anatomy of muscle insertions with the utilization of Shape Memory Alloy (SMA) wires' muscle-like actuation properties, the bonding in-between the flexor tendons and extensor tendons allows for the realization of the central and lateral band of the finger anatomy. Evaluated using the computer vision method, the proposed robotic finger demonstrates a range of motion (ROM) equivalent to 113%, 87% and 88% of the human dynamic ROM for the DIP, PIP and MCP joints, respectively. The proposed finger possesses a soft nature when relaxed and becomes firm when activated, pioneering a new approach in biomimetic robot design and offering a unique contribution to the future of generalist humanoid robots.

Does Bistability Improve Swimming Performance in Robotic Fish?

A bistable mechanism has two stable states with energy input required to move from one stable state to another. This energy barrier allows for energy storage and release which can be used to improve systems characteristics. Bistability has been used to increase the frequency range over which a kinetic energy harvester is effective, and it has been proposed that bistability can increase the efficiency of biomimetic swimming robots. However, experiments involving bistable swimming robots have typically used bistability as a means of overcoming limitations inherent to soft actuators, rather than to increase overall performance. This article implements bistability into a swimming robotic and compares performance with and without bistable action. The static thrust generation and power consumption for bistable and nonbistable configurations for five different tail morphologies are compared. Bistability is generally found to increase the system efficiency, particularly at lower frequencies where increases are observed up to 250%. The untethered swimming speed of the robot in open water is also found to increase by approximately 30%. The results show that bistability can offer direct performance benefits for biomimetic swimming, but that the bistable transmission must be well tuned to the dynamics of the rest of the system.