Search Results (1,211)

In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases. Full article

Radiative cooling is a promising strategy to address energy challenges arising from global warming. Nevertheless, integrating optimal cooling performance with commercial applications is a considerable challenge. Here, we demonstrate a scalable and straightforward approach for fabricating green radiative cooling coating consisting of methyl cellulose matrix-random diatomites with water as a solvent. Because of the efficient scattering of the porous morphology of diatomite and the inherent absorption properties of both diatomite and cellulose, the aqueous coating exhibits an excellent solar reflectance of 94% in the range of 0.25–2.5 μm and a thermal emissivity of 0.9 in the range of 8–14 µm. During exposure to direct sunlight at noon, the obtained coating achieved a maximum subambient temperature drop of 6.1 °C on sunny days and 2.5 °C on cloudy days. Furthermore, diatomite is a naturally sourced material that requires minimal pre-processing, and our coatings can be prepared free from harmful organic compounds. Combined with cost-effectiveness and environmental friendliness, it offers a viable path for the commercial application of radiative cooling. Full article

This paper presents a monocular biological microscope with colorful 3D reconstruction and an extended depth of field using an electrically tunable lens. It is based on a 4f optical system with an electrically tunable lens at the confocal plane. Rapid and extensive depth scanning while maintaining consistent magnification without mechanical movement is achieved. We propose an improved Laplacian operator that considers pixels in diagonal directions to provide enhanced fusion effects and obtain more details of the object. Accurate 3D reconstruction is achieved using the shape-from-focus method by tuning the focal power of the electrically tunable lens. We validate the proposed method by performing experiments on biological samples. The 3D reconstructed images obtained from the biological samples match the actual shrimp larvae and bee antenna samples. Two standard gauge blocks are used to evaluate the 3D reconstruction performance of the proposed method. The experimental results show that the extended depth of fields are 120 µm, 240 µm, and 1440 µm for shrimp larvae, bee tentacle samples, and gauge blocks, respectively. The maximum absolute errors are −39.9 μm and −30.6 μm for the first and second gauge blocks, which indicates 3D reconstruction deviations are 0.78% and 1.52%, respectively. Since the procedure does not require any custom hardware, it can be used to transform a biological microscope into one that effectively extends the depth of field and achieves highly accurate 3D reconstruction results, as long as the requirements are met. Such a microscope presents a broad range of applications, such as biological detection and microbiological diagnosis, where colorful 3D reconstruction and an extended depth of field are critical. Full article

In the field of three-dimensional object design and fabrication, this paper explores the transformative potential at the intersection of biomaterials, biopolymers, and additive manufacturing. Drawing inspiration from the intricate designs found in the natural world, this study contributes to the evolving landscape of manufacturing and design paradigms. Biomimicry, rooted in emulating nature’s sophisticated solutions, serves as the foundational framework for developing materials endowed with remarkable characteristics, including adaptability, responsiveness, and self-transformation. These advanced engineered biomimetic materials, featuring attributes such as shape memory and self-healing properties, undergo rigorous synthesis and characterization procedures, with the overarching goal of seamless integration into the field of additive manufacturing. The resulting synergy between advanced manufacturing techniques and nature-inspired materials promises to revolutionize the production of objects capable of dynamic responses to environmental stimuli. Extending beyond the confines of laboratory experimentation, these self-transforming objects hold significant potential across diverse industries, showcasing innovative applications with profound implications for object design and fabrication. Through the reduction of waste generation, minimization of energy consumption, and the reduction of environmental footprint, the integration of biomaterials, biopolymers, and additive manufacturing signifies a pivotal step towards fostering ecologically conscious design and manufacturing practices. Within this context, inanimate three-dimensional objects will possess the ability to transcend their static nature and emerge as dynamic entities capable of evolution, self-repair, and adaptive responses in harmony with their surroundings. The confluence of biomimicry and additive manufacturing techniques establishes a seminal precedent for a profound reconfiguration of contemporary approaches to design, manufacturing, and ecological stewardship, thereby decisively shaping a more resilient and innovative global milieu. Full article

Pistol shrimp generate cavitation bubbles. Cavitation impacts due to bubble collapses are harmful phenomena, as they cause severe damage to hydraulic machinery such as pumps and valves. However, cavitation impacts can be utilized for mechanical surface treatment to improve the fatigue strength of metallic materials, which is called “cavitation peening”. Through conventional cavitation peening, cavitation is generated by a submerged water jet, i.e., a cavitating jet or a pulsed laser. The fatigue strength of magnesium alloy when treated by the pulsed laser is larger than that of the jet. In order to drastically increase the processing efficiency of cavitation peening, the mechanism of pistol shrimp (specifically when used to create a cavitation bubble), i.e., Alpheus randalli, was quantitatively investigated. It was found that a pulsed water jet generates a cavitation bubble when a shrimp snaps its claws. Furthermore, two types of cavitation generators were developed, namely, one that uses a pulsed laser and one that uses a piezo actuator, and this was achieved by mimicking a pistol shrimp. The generation of cavitation bubbles was demonstrated by using both types of cavitation generators: the pulsed laser and the piezo actuator. Full article

The intricate and highly complex morphologies of diatom frustules have long captured the attention of biomimetic researchers, initiating innovation in engineering solutions. This study investigates the potential of diatom-inspired surface stiffeners to determine whether the introduced innovative strategy is a viable alternative for addressing engineering challenges demanding enhanced stiffness. This interdisciplinary study focuses on the computer-aided generation of stress-adaptive lightweight structures aimed at optimizing bending stiffness. Through a comprehensive microscopical analysis, morphological characteristics of diatom frustules were identified and abstracted to be applied to a reference model using computer-aided methods and simulated to analyze their mechanical behavior under load-bearing conditions. Afterwards, the models are compared against a conventional engineering approach. The most promising biomimetic approach is successfully automated, extending its applicability to non-planar surfaces and diverse boundary conditions. It yields notable improvement in bending stiffness, which manifests in a decrease of displacement by approximately 93% in comparison to the reference model with an equivalent total mass. Nonetheless, for the specific load case considered, the engineering approach yields the least displacement. Although certain applications may favor conventional methods, the presented approach holds promise for scenarios subjected to varying stresses, necessitating lightweight and robust solutions. Full article

Research into how fish and other aquatic organisms propel themselves offers valuable natural references for enhancing technology related to underwater devices like vehicles, propellers, and biomimetic robotics. Additionally, such research provides insights into fish evolution and ecological dynamics. This work carried out a numerical investigation of the most relevant dimensionless parameters in a fish swimming environment (Reynolds $Re$, Strouhal $St$, and $Slip$ numbers) to provide valuable knowledge in terms of biomechanics behavior. Thus, a three-dimensional numerical study of the fish-like lambari, a BCF swimmer with carangiform kinematics, was conducted using the URANS approach with the k-$\omega$-SST transition turbulence closure model in the OpenFOAM software. In this study, we initially reported the equilibrium Strouhal number, which is represented by $S{t}^{\ast }$, and its dependence on the Reynolds number, denoted as $Re$. This was performed following a power–law relationship of $St\propto R{e}^{(-\alpha )}$. We also conducted a comprehensive analysis of the hydrodynamic forces and the effect of body undulation in fish on the production of swimming drag and thrust. Additionally, we computed propulsive and quasi-propulsive efficiencies, as well as examined the influence of the Reynolds number and $Slip$ number on fish performance. Finally, we performed a vortex dynamics analysis, in which different wake configurations were revealed under variations of the dimensionless parameters $St$, $Re$, and $Slip$. Furthermore, we explored the relationship between the generation of a leading-edge vortex via the caudal fin and the peak thrust production within the motion cycle. Full article

We have designed and synthesized a series of bioinspired pyrano[2,3-f]coumarin-based Calanolide A analogs with anti-HIV activity. The design of these new calanolide analogs involved incorporating nitrogen heterocycles or aromatic groups in lieu of ring C, effectively mimicking and preserving their bioactive properties. Three directions for the synthesis were explored: reaction of 5-hydroxy-2,2-dimethyl-10-propyl-2H,8H-pyrano[2,3-f]chromen-8-one with (i) 1,2,4-triazines, (ii) sulfonylation followed by Suzuki cross-coupling with (het)aryl boronic acids, and (iii) aminomethylation by Mannich reaction. Antiviral assay of the synthesized compounds showed that compound 4 has moderate activity against HIV-1 on enzymes and poor activity on the cell model. A molecular docking study demonstrates a good correlation between in silico and in vitro HIV-1 reverse transcriptase (RT) activity of the compounds when docked to the nonnucleoside RT inhibitor binding site, and alternative binding modes of the considered analogs of Calanolide A were established. Full article

The wings of birds exhibit multi-degree-of-freedom motions during flight. Among them, the flapping folding motion and chordwise passive deformation of the wings are prominent features of large birds in flight, contributing to their exceptional flight capabilities. This article presents a method for the fast and accurate calculation of folding passive torsional flapping wings in the early design stage. The method utilizes the unsteady three-dimensional panel method to solve the aerodynamic force and the linear beam element model to analyze the fluid–structure coupling problem. Performance comparisons of folding flapping wings with different kinematics are conducted, and the effects of various kinematic parameters on folding flapping wings are analyzed. The results indicate that kinematic parameters significantly influence the lift coefficient, thrust coefficient, and propulsion efficiency. Selecting the appropriate kinematic and geometric parameters is crucial for enhancing the efficiency of the folding flapping wing. Full article

The essence of biomimetics in human–computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR’s kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms. Full article

In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings’ remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application. Full article

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor. Full article

Inspired by the natural skeletal muscles, this paper presents a novel shape memory alloy-based artificial muscle matrix (AMM) with advantages of a large output force and displacement, flexibility, and compactness. According to the composition of the AMM, we propose a matrix control strategy to achieve independent control of the output force and displacement of the AMM. Based on the kinematics simulation and experiments, we obtained the output displacement and bearing capacity of the smart digital structure (SDS) and confirmed the effectiveness of the matrix control strategy to achieve force and displacement output independently and controllably. A bionic mechanical ankle actuated by AMM was proposed to demonstrate the actuating capability of the AMM. Experimental results show that the angle and force of the bionic mechanical ankle are output independently and have a significant gradient. In addition, by using a self-sensing method (resistance self-feedback) and PD control strategy, the output angle and force of the bionic mechanical ankle can be maintained for a long time without overheating of the AMM. Full article

Reinforcement learning (RL)-based controllers have been applied to the high-speed movement of quadruped robots on uneven terrains. The external disturbances increase as the robot moves faster on such terrains, affecting the stability of the robot. Many existing RL-based methods adopt higher control frequencies to respond quickly to the disturbance, which requires a significant computational cost. We propose a control framework that consists of an RL-based control policy updating at a low frequency and a model-based joint controller updating at a high frequency. Unlike previous methods, our policy outputs the control law for each joint, executed by the corresponding high-frequency joint controller to reduce the impact of external disturbances on the robot. We evaluated our method on various simulated terrains with height differences of up to 6 cm. We achieved a running motion of $1.8$ m/s in the simulation using the Unitree A1 quadruped. The RL-based control policy updates at 50 Hz with a latency of 20 ms, while the model-based joint controller runs at 1000 Hz. The experimental results show that the proposed framework can overcome the latency caused by low-frequency updates, making it applicable for real-robot deployment. Full article