Biomimetics最新文献

Untethered magnetic soft robots show great potential for biomedical and small-scale micromanipulation applications due to their high flexibility and ability to cause minimal damage. However, most current research on these robots focuses on marine and reptilian biomimicry, which limits their ability to move in unstructured environments. In this work, we design a quadruped soft robot with a magnetic top cover and a specific magnetization angle, drawing inspiration from the common locomotion patterns of quadrupeds in nature and integrating our unique actuation principle. It can crawl and tumble and, by adjusting the magnetic field parameters, it adapts its locomotion to environmental conditions, enabling it to cross obstacles and perform remote transportation and release of cargo.

Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method.

Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture wing trajectory data from five free-flying pigeons (Columba livia). Five key motion parameters are used to quantitatively characterize wing kinematics: flapping, sweeping, twisting, folding and bending. In addition, the forelimb skeleton is mapped using an open-chain three-bar mechanism model. By systematically evaluating the relationship of joint degrees of freedom (DOFs), we configured the model as a 3-DOF shoulder, 1-DOF elbow and 2-DOF wrist. Based on the correlation analysis between wingbeat kinematics and joint movement, we found that the strongly correlated shoulder and wrist roll within the stroke plane cause wing flap and bending. There is also a strong correlation between shoulder, elbow and wrist yaw out of the stroke plane, which causes wing sweep and fold. By simplifying the wing morphing, we developed three flapping wing robots, each with different DOFs inside and outside the stroke plane. This study provides insight into the design of flapping wing robots capable of mimicking the 3D wing motion of pigeons.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

In today's society, robots are increasingly being developed and playing an important role in many fields of industry. Combined with advances in artificial intelligence, sensors, and design principles, these robots are becoming smarter, more flexible, and especially capable of interacting more naturally with humans. In that context, a comprehensive humanoid robot with human-like actions and emotions has been designed to move flexibly like a human, performing movements to simulate the movements of the human neck and head so that the robot can interact with the surrounding environment. The mechanical design of the emotional humanoid robot head focuses on the natural and flexible movement of human electric motors, including flexible suitable connections, precise motors, and feedback signals. The feedback control parts, such as the neck, eyes, eyebrows, and mouth, are especially combined with artificial skin to create a human-like appearance. This study aims to contribute to the field of biomimetic humanoid robotics by developing a comprehensive design for a humanoid robot head with human-like actions and emotions, as well as evaluating the effectiveness of the motor and feedback control system in simulating human behavior and emotional expression, thereby enhancing natural interaction between robots and humans. Experimental results from the survey showed that the behavioral simulation rate reached 94.72%, and the emotional expression rate was 91.50%.

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method.

In this work, micro air vehicles (MAVs) equipped with bio-inspired wings are investigated experimentally in wind tunnel. The starting point is that insects such as dragonflies, butterflies and locusts have wings with rigid tubular elements (corrugation) connected by flexible parts (profiling). So far, it is important to understand the specific aerodynamic effects of corrugation and profiling as applied to conventional wings for the optimization of low-Reynolds-number aerodynamics. The present study, in comparison to previous investigations on the topic, considers whole MAVs rather than isolated wings. A planform with a low aperture-to-chord ratio is employed in order to investigate the interaction between large tip vortices and the flow over the wing surface at large angles of incidence. Comparisons are made by measuring global aerodynamic loads using force balance, specifically drag and lift, and detailed local velocity fields over wing surfaces, by means of particle image velocimetry (PIV). This type of combined global-local investigation allows describing and relating overall MAV performance to detailed high-resolution flow fields. The results indicate that the combination of wing corrugation and profiling gives effective enhancements in performance, around 50%, in comparison to the classical flat-plate configuration. These results are particularly relevant in the framework of low-aspect-ratio MAVs, undergoing beneficial interactions between tip vortices and large-scale separation.

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a prevalent issue in extensive indoor environments. The study explores various scenarios, comparing the performance of the base CNN and meta-learning models. The meta-learning approach involves few-shot learning tasks, such as three-way N-shot, five-way N-shot, etc., to enhance the model's ability to generalize from limited data. The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. The scenarios included both base classes and novel classes, with and without meta-learning. The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. With 20 samples per class, the base CNN achieved an accuracy of 0.80 during the pre-training stage, while meta-learning (three-way one-shot) achieved an accuracy of 0.78 on a new small dataset with novel classes.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based on deep reinforcement learning. The algorithm utilizes a training framework comprising two neural network modules (actor network and critic network) to enhance training performance. The framework can control jumping by directly mapping the robot's observations (robot position and velocity, obstacle position, target position, etc.) to its joint torques. The control policy increases randomness and exploration by introducing an entropy term to the policy function. Moreover, we designed a stage incentive mechanism to adjust the reward function dynamically, thereby improving the robot's jumping stability and accuracy. We established a locus-inspired jumping robot platform and conducted a series of jumping experiments in simulation. The results indicate that the robot could perform smooth and non-flip jumps, with the error of the distance from the target remaining below 3%. The robot consumed 44.6% less energy to travel the same distance by jumping compared with walking. Additionally, the proposed algorithm exhibited a faster convergence rate and improved convergence effects compared with other classical algorithms.