Bioinspiration & Biomimetics最新文献

Social robots have been widely used to deliver emotional, cognitive and social support to humans. The exchange of affective gestures, instead, has been explored to a lesser extent, despite phyisical interaction with social robots could provide the same benefits as human-human interaction. Some studies that explored the touch and hugs gestures were found in literature, but there are no studies that investigate the possibility of delivering realistic caress gestures, which are, in turn, the easiest affective gestures that could be delivered with a robot. The first objective of this work was to study the kinematic and dynamic features of the caress gesture by conducting experimental acquisitions in which ten healthy volunteers were asked to caress the cheek of a mannequin in two conditions, i.e. standing and sitting. Average motion and force features were then analyzed and used to generate a realistic caress gesture with an anthropomorphic robot, with the aim of assessing the feasibility of reproducing the caress gesture with a robotic device. In addition, twenty-six healthy volunteers evaluated the anthropomorphism and perceived safety of the reproduced affective gesture by answering the Godspeed Questionnaire Series and a list of statements on the robot motion. The gesture reproduced by the robot was similar to the caress gesture performed by healthy volunteers both in terms of hand trajectory and orientation, and exchanged forces. Overall, volunteers perceived the robot motion as safe and positive emotions were elicited. The proposed approach could be adapted to humanoid robots to improve the perceived anthropomorphism and safety of the caress gesture.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the acute angle test (AAT) is proposed, which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighbor selection mechanism can reduce the redundant communication burden between large-scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

Over the last two decades, robotics engineering has witnessed rapid growth in the exploration and development of soft robots. Soft robots are made of deformable materials with mechanical properties or other features that resemble biological structures. These robots are often inspired by living organisms or mimic their locomotion, such as crawling and swimming. This paper aims to assist researchers in robotics and engineering to design soft robots incorporating or inspired by biological systems with a more informed perspective on biological models and functions. We address the characteristics of fluidic soft robots inspired by or mimicking biological examples, establish a method to categorize soft robots from a functional biological perspective, and provide a wider range of organisms to inspire the development of soft robotics. The actuation mechanisms in bioinspired and biomimetic soft robotics would benefit from a clearer understanding of the underlying principles, organization, and function of biological structures.

In recent years, the micro air vehicle (MAV) oscillations caused by thrust imbalances have received more attention. This paper proposes a dual-wing thrust balance model (DTBM) that can solve the above problem by iterating the modified rotation angle formula. The core control parameter of the DTBM model is the au angle, which refers to the angle between the wing surface and the stroke plane at the mid-stroke position during the upstroke. For each degree change in the au angle, the range of variation in the dimensionless average thrust coefficient is between 0.0225-0.0268. A thrust coefficient of 0.0225 causes the dragonfly to move forward by 9.037 cm in one second, which is equivalent to 1.29 times its body length. By using DTBM, the average thrust coefficient can be reduced to below 0.001 in just a few iterations. No matter how complex the motion pattern is, the DTBM can achieve thrust balance within 0.278 s. Through our research, when selecting the deviation angle motion of real dragonflies, the dual-wing au angles exhibit a highly linear correlation with wing spacing, called linear motion. In contrast, the nonlinear variation of the au angle appears in the hindwing of the no-deviation motion and the forewing of the elliptical deviation motion. All of the nonlinear changes are referred to as nonlinear motion. Nonlinear variation of the au angle arises from larger disturbances of the lateral force during the upstroke. The stronger lateral force is closely related to the flapping trajectory. When the flapping trajectory causes the dual-wing to closely approach each other in the mid-stroke, a continuous positive pressure zone forms between the dual-wing. The collision of the leading-edge vortex and the shedding of the trailing-edge vortex is the special flow field structure in the nonlinear motion. Guided by the DTBM, future designs of MAVs will be able to better achieve thrust balance during hovering flight, requiring only the embedding of the iteration algorithm and prediction function of the DTBM in the internal chip.

Flying insects, such as flies and bees, have evolved the capability to rely solely on visual cues for smooth and secure landings on various surfaces. In the process of carrying out tasks, micro unmanned aerial vehicles (UAVs) may encounter various emergencies, and it is necessary to land safely in complex and unpredictable ground environments, especially when altitude information is not accurately obtained, which undoubtedly poses a significant challenge. Our study draws on the remarkable response mechanism of the Lobula Giant Movement Detector to looming scenarios to develop a novel UAV landing strategy. The proposed strategy does not require distance estimation, making it particularly suitable for payload-constrained micro aerial vehicles. Through a series of experiments, this strategy has proven to effectively achieve stable and high-performance landings in unknown and complex environments using only a monocular camera. Furthermore, a novel mechanism to trigger the final landing phase has been introduced, further ensuring the safe and stable touchdown of the drone.

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.

Probes that penetrate soil are used in fields such as geotechnical engineering, agriculture, and ecology to classify soils and characterize their propertiesin situ. Conventional tools such as the Cone Penetration Test (CPT) often face challenges due to the lack of reaction force needed to penetrate stiff or dense soil layers, necessitating the use of large drill rigs. This paper investigates more efficient means of penetrating soil by taking inspiration from a plant-root motion known as circumnutation. Experimental penetration tests on sands are performed with circumnutation-inspired (CI) probes that advance at a constant vertical velocity (v) while simultaneously rotating at a constant angular velocity (ω). These probes have bent tips with a given bent angle (α) and bent length (L1). The variation of the mobilized vertical force (Fz), torque (Tz.), and the mechanical work components with the ratio of tangential to vertical velocity (ωR/ν, whereRis the distance of the tip of the probe from the vertical axis of rotation) is investigated along with the effects of probe geometry, vertical velocity, and soil relative density (DR). The results show that the soil penetration resistance does not vary withv, but it increases asα,L1, andDRare increased.Fzdecays exponentially with increasingωR/v,Tzinitially increases and then plateaus, while total work (WT) shows little magnitude changes initially but later increases monotonically. The mechanisms leading to these trends are identified as the changes in the probe projected areas and mobilized normal stresses due to differences in probe geometry and the effects ofωR/von the resultant force direction and soil disturbance. The results show that CI penetration within a specific range ofωR/vleads to small increases inWT(i.e.,⩽25%), yet mobilizesFzmagnitudes that are 50%-80% lower than that mobilized during non-rotational penetration (i.e., CPT). This indicates that CI penetration can be adopted forin situcharacterization or sensor placement with smaller vertical forces, allowing for use of lighter rigs.

This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the 'vine-robot', using the Euler-Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environments by extending its body. We derive the dynamic equations of motion and employ model predictive control to regulate the task space position, orientation, and interaction forces. Simulation experiments are conducted to evaluate the performance of the proposed model and control strategy. The results demonstrate that the model effectively achieves sub-millimeter precision in the position control in both static and time varying refrence trajectroies, and sub micronewton in force control.

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

Parylene-coated carbon fiber (CF) arrays with tunable inclination angles and heights were fabricated using oxygen plasma etching of composite wafers with embedded parallel CFs, followed by parylene coating via chemical vapor deposition. The effective elastic modulus of the CF arrays was found to decrease approximately in proportion to the square of the fiber length (5-60μm), with the parylene coating (∼2μm) further slightly reducing the modulus. Both experimental measurements and finite element simulations indicated that CF arrays with inclination angles below 75° exhibit ideal contact with glass wafers during electrostatic adhesion. However, the measured electrostatic adhesion between CF arrays and A4 paper was significantly lower than the predicted value for ideal contact, likely due to the porous nature of the paper. Electrostatic chuck prototypes based on the parylene-coated CF arrays demonstrated effective pick-and-place capabilities for A4 paper, plastic films, and glass wafers at voltages ranging from 500 to 900 V, without causing surface damage or leaving residue. These results highlight the potential of the parylene-coated CF arrays for applications in high-precision manufacturing and automated handling systems.