Search Results (429)

Neuromorphic computing, an interdisciplinary field combining neuroscience and computer science, aims to create efficient, bio-inspired systems. Different from von Neumann architectures, neuromorphic systems integrate memory and processing units to enable parallel, event-driven computation. By simulating the behavior of biological neurons and networks, these systems excel in tasks like pattern recognition, perception, and decision-making. Neuromorphic computing chips, which operate similarly to the human brain, offer significant potential for enhancing the performance and energy efficiency of bio-inspired algorithms. This review introduces a novel five-dimensional comparative framework—process technology, scale, power consumption, neuronal models, and architectural features—that systematically categorizes and contrasts neuromorphic implementations beyond existing surveys. We analyze notable neuromorphic chips, such as BrainScaleS, SpiNNaker, TrueNorth, and Loihi, comparing their scale, power consumption, and computational models. The paper also explores the applications of neuromorphic computing chips in artificial intelligence (AI), robotics, neuroscience, and adaptive control systems, while facing challenges related to hardware limitations, algorithms, and system scalability and integration. Full article

In a mother–child underwater bio-inspired robotic system, the equivalent lever arm between the master and slave inertial navigation systems (INSs) varies with launcher attitude changes and structural flexure. This time-varying lever arm introduces hard-to-model systematic velocity errors that degrade the accuracy and filter convergence of velocity difference-based transfer alignment. Traditional rigid body compensation relies on precise, constant lever-arm parameters and fails when booms, launch tubes, or flexible manipulators undergo appreciable deformation or reconfiguration. To address this, we augment a “velocity–attitude joint matching and innovation-based adaptive Kalman filter (AKF)” framework with an attention-based Long Short-Term Memory (LSTM) feed-forward module. Using only a short, real-time Inertial Measurement Unit (IMU) sequence from the slave INS, the module predicts and compensates the velocity bias induced by the lever arm. Numerical simulations of an underwater bio-inspired robot deployment scenario show that, under typical maneuvers (acceleration, turning, fin-flapping, and S-curve), the proposed method reduces the root-mean-square (RMS) misalignment angle error from about 14.5′ to 5.2′ and the RMS installation error angle from 8.8′ to 3.0′—average reductions of about 64% and 66%, respectively—substantially improving the robustness and practical applicability of transfer alignment under time-varying lever arms and flexible disturbances. Full article

To overcome the limitations in maneuverability and adaptability of traditional underwater vehicles, a novel hybrid-actuated, multimodal cephalopod-inspired robot is proposed. This robot innovatively integrates a hybrid drive system wherein sinusoidal undulating fins provide primary propulsion and steering, water-flapping tentacles offer auxiliary burst propulsion, and a gear-and-rack center-of-gravity (CoG) adjustment module modulates the pitch angle to enable depth control through hydrodynamic lift during forward motion. The effectiveness of the design was validated through a series of experiments. Thrust tests demonstrated that the undulating fin thrust scales quadratically with oscillation frequency, aligning with hydrodynamic theory. Mobility experiments confirmed the multi-degree-of-freedom control of the robot, demonstrating effective diving and surfacing via the CoG module and high maneuverability, achieving a turning radius of approximately 15 cm through differential fin control. Furthermore, field trials in an outdoor artificial lake with a depth of less than 1 m validated its environmental robustness. These results confirm the versatile maneuvering capabilities of the robot and its robust adaptability to confined and shallow-water environments, presenting a novel platform for complex underwater observation tasks. Full article

Exploring the design of beneficial nonlinear restoring force structures has become a highly popular topic due to their extensive applications in energy harvesting, actuation, energy absorption, robotics, etc. However, the current literature lacks a systematic review and classification that addresses the design, modeling, and parameter identification of nonlinear restoring forces. Thus, the present paper provides a thorough examination of the latest advancements in the design of nonlinear restoring forces, as well as modeling and parameter identification in contemporary beneficial nonlinear designs. The seven design methodologies, namely magnetic coupling, oblique spring linkages, static or dynamic preloading, metamaterials, bio-inspired, MEMS (Micro-Electromechanical Systems) manufacturing, and dry friction applied approaches, are classified. The polynomial, hysteretic, and piecewise linear models are summarized for nonlinear restoring force characterization. The system parameter identification methods covering restoring force surface, Hilbert transform, time-frequency analysis, nonlinear subspace identification, unscented Kalman filter, optimization algorithms, physics-informed neural networks, and data-driven sparse regression are reviewed. Moreover, possible enhancement strategies for nonlinear system identification of nonlinear restoring forces are presented. Finally, broader implications and future directions for the design, characterization, and identification of nonlinear restoring forces are discussed. Full article

The increasing complexity of marine operations has intensified the need for intelligent robotic systems to support ocean observation, exploration, and resource management. Underwater swarm robotics offers a promising framework that extends the capabilities of individual autonomous platforms through collective coordination. Inspired by natural systems, such as fish schools and insect colonies, bio-inspired swarm approaches enable distributed decision-making, adaptability, and resilience under challenging marine conditions. Yet research in this field remains fragmented, with limited integration across algorithmic, communication, and hardware design perspectives. This review synthesises bio-inspired coordination mechanisms, communication strategies, and system design considerations for underwater swarm robotics. It examines key marine-specific algorithms, including the Artificial Fish Swarm Algorithm, Whale Optimisation Algorithm, Coral Reef Optimisation, and Marine Predators Algorithm, highlighting their applications in formation control, task allocation, and environmental interaction. The review also analyses communication constraints unique to the underwater domain and emerging acoustic, optical, and hybrid solutions that support cooperative operation. Additionally, it examines hardware and system design advances that enhance system efficiency and scalability. A multi-dimensional classification framework evaluates existing approaches across communication dependency, environmental adaptability, energy efficiency, and swarm scalability. Through this integrated analysis, the review unifies bio-inspired coordination algorithms, communication modalities, and system design approaches. It also identifies converging trends, key challenges, and future research directions for real-world deployment of underwater swarm systems. Full article

We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible manufacturing setup. Firstly, we investigated human psychology and biomechanics (kinetics and kinematics) for human-to-robot handover of an object in the human–robot collaborative set-up in three separate experimental conditions: (i) human possessed high trust in the robot, (ii) human possessed moderate trust in the robot, and (iii) human possessed low trust in the robot. The results showed that human psychology was significantly impacted by human trust in the robot, which also impacted the biomechanics of human-to-robot handover, i.e., human hand movement slowed down, the angle between human hand and robot arm increased (formed a braced handover configuration), and human grip forces increased if human trust in the robot decreased, and vice versa. Secondly, being inspired by those empirical results related to human psychology and biomechanics, we proposed a novel robot-to-human object handover mechanism (strategy). According to the novel handover mechanism, the robot varied its handover configurations and motions through kinematic redundancy with the aim of reducing potential impulse forces on the human body through the object during the handover when robot trust in the human was low. We implemented the proposed robot-to-human handover mechanism in the human–robot collaborative assembly task in the hybrid cell. The experimental evaluation results showed significant improvements in human–robot interaction (HRI) in terms of transparency, naturalness, engagement, cooperation, cognitive workload, and human trust in the robot, and in overall performance in terms of handover safety, handover success rate, and assembly efficiency. The results can help design and develop human–robot handover mechanisms for human–robot collaborative tasks in various applications such as industrial manufacturing and manipulation, medical surgery, warehouse, transport, logistics, construction, machine shops, goods delivery, etc. Full article

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration. Full article

Banana harvesting relies heavily on manual labor, which is labor-intensive and prone to fruit damage due to insufficient control of contact forces. This paper presents a systematic methodology for the design and optimization of adaptive flexible end-effectors for banana bunch harvesting, focusing on contact behavior and mechanical constraints. By integrating response surface methodology (RSM) with multi-objective genetic algorithm (MOGA) optimization, the relationships between finger geometry parameters and key performance metrics—contact area, contact stress, and radial stiffness—were quantified, and Pareto-optimal structural configurations were identified. Experimental and simulation results demonstrate that the optimized flexible fingers effectively improve handling performance: contact area increased by 13–28%, contact stress reduced by 45–56%, and radial stiffness enhanced by 193%, while the maximum shear stress on the fruit stalk decreased by 90%, ensuring harvesting stability during dynamic loading. The optimization effectively distributes contact pressure, minimizes fruit damage, and enhances grasping reliability. The proposed contact-behavior-constrained design framework enables passive adaptation to fruit morphology without complex sensors, offering a generalizable solution for soft robotic handling of fragile and irregular agricultural products. This work bridges the gap between bio-inspired gripper design and practical agricultural application, providing both theoretical insights and engineering guidance for automated, low-damage fruit harvesting systems. Full article

In recent years, the demand for mobile robots to perform odor source localization tasks in dangerous environments has been increasing, and this technology has gradually become a focus of research. However, existing bio-inspired algorithms still have many limitations in real applications. Therefore, we attempt to design a moth-inspired algorithm integrated with a fuzzy control mechanism to enhance the robot’s ability to track odor sources in environments with dense obstacles. The goal is to improve the accuracy and efficiency of localization. Through multiple sets of simulation and real environment experiments, we observed that this algorithm achieved significant improvements in multiple indicators, including task success rate, search efficiency, and path planning quality. Compared with the traditional moth algorithm, its stability and adaptability in complex scenarios are also outstanding. Full article

Humanoid robots have gained public awareness and intrigue over the last few years. During this time, there has been a greater push to develop robots to behave more like humans, not just in how they speak but also in how they move. A novel humanoid robotic head-and-neck platform designed to facilitate the investigation of movement characteristics is proposed. The research presented here aims to characterise the motion of a humanoid robotic head, Aquila, to aid the development of humanoid robots with head movements more similar to those of humans. This platform also aims to promote further studies in human head motion. This paper proposes a design for a humanoid robotic head platform capable of performing three principal human motion patterns: yaw, pitch, and roll. Using the Arduino IDE (2.3.2) and MATLAB/Simulink (2024b), all three types of movement were implemented and tested with various parameters. Each type of movement is quantified in terms of range, stability, and dynamic response using time-series data collected over 35 s of continuous observation. The results demonstrate that a humanoid robot head can mimic the range of displacement of a healthy human subject but does not exhibit the same smoothness and micro-adjustments observed in dynamic human head movements. An RMSE of under 0.3 rad is achieved for each motion axis—pitch, roll, and yaw—when comparing robotic head movement to human head movement. The investigation of preliminary results highlights the need for further system optimisation. This paper’s conclusion highlights that the bio-inspired control concept, paired with the proposed 8-stepper motor platform, enhances realism and interaction in the context of head movement in robotic systems. Full article

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

Reversible adhesives enable temporary yet robust bonding between surfaces, allowing controlled detachment without structural or interfacial damage. This capability is gaining increasing recognition as a crucial requirement for sustainable technologies, where repairability, reusability, and minimal waste are key objectives. Among the diverse strategies explored for reversible adhesion (including supramolecular assemblies, bioinspired dry adhesives, and stimuli-responsive polymers), hydrogel-based systems have emerged as particularly versatile candidates due to their tunable mechanics, elasticity, and intrinsic biocompatibility. Recent studies highlight the use of renewable or biodegradable polymers to develop sustainable, water-rich hydrogel networks with controllable adhesive properties, minimizing environmental impact while maintaining performance. Despite these advances, significant challenges still hinder full implementation: biopolymer-based systems such as chitosan or starch often exhibit strong but poorly controllable adhesion, compromising reversibility and reusability. This review provides a comprehensive overview of strategies for developing hydrogel-based reversible adhesives, focusing on sustainable material selection, molecular design principles, and the underlying mechanisms of bonding and debonding. Furthermore, characterization methodologies, from conventional mechanical testing to surface-sensitive and dynamic techniques, are discussed in detail to establish structure–property–function relationships. Finally, emerging directions and application opportunities are outlined, offering a framework for the rational design of next-generation, sustainable adhesive systems. Full article

Flapping-wing micro-air vehicles (FWMAVs) exhibit unique aerodynamic characteristics that differ fundamentally from other aircraft, yet little is known about their dynamic stability derivatives. This study aims to identify pitch-rate stability derivatives of an in-house prototype, CKopter-1, to advance the modeling and control of bio-inspired flight. Experiments were conducted using a robotic-arm fan-array system that enabled prescribed pitching motions under controlled inflow. Aerodynamic forces and moments were measured with a six-axis load cell, while vehicle kinematics were captured using motion tracking and synchronized during post-processing. Tests consisted of quasi-static cycles and dynamic cycles at pitch rates of 35°/s, 58.8°/s, and 68.4°/s. The results revealed static instability below an angle of attack of 33°, a trim condition near 58.5°, and positive stability up to 72.5°. Dynamic cases showed clear pitch-rate effects in the longitudinal components, from which the derivatives were extracted. A comparison with previous studies confirmed comparable magnitudes, with systematic differences attributable to wing dihedral and tail length. This study demonstrates that the fan-array robotic-arm method enables stability derivative identification even beyond feasible flight regimes, providing valuable parameters for future flight dynamics modeling and control of FWMAVs. Full article

The automation of wind turbine maintenance processes is aimed at improving the operational efficiency of wind farms through timely diagnosis of technical condition, predictive identification of potential failures, and optimization of the distribution of repair and restoration procedures. In this context, the main objective of the study is to improve the reliability and efficiency of wind energy infrastructure by developing an intelligent decision support system for wind turbine maintenance. The proposed architecture includes a module for optimizing the routes of robotic agents, which implements a hybrid method based on a combination of the A* algorithm and a modified ant algorithm with dynamic pheromone updating and B-spline trajectory smoothing, as well as a module for detecting based on a modified YOLOv3 model with integrated adaptive feature fusion and bio-inspired anchor frame optimization. The choice of the YOLOv3 architecture is due to the optimal balance between accuracy and inference speed on embedded platforms of robotic autonomous agents, which ensures the functioning of the detection module in real time with limited computing resources. The results of the computational experiment confirmed a 15–20% reduction in route length and energy consumption, as well as a 41% increase in the $F1$ detection metric relative to the baseline implementation of YOLOv3 while maintaining a performance of 42 frames per second. The set of results obtained confirms the practical feasibility and integration potential of the developed architecture into the predictive maintenance and life cycle management of wind energy infrastructure. Full article

The increasing inclusion of robots in social areas requires continuous improvement of behavioral strategies that robots must follow. Although behavioral strategies mainly focus on operational efficiency, other aspects should be considered to provide a reliable interaction in terms of sociability (e.g., methods for detection and interpretation of human behaviors, how and where human–robot interaction is performed, and participant evaluation of robot behavior). This scoping review aims to answer seven research questions related to robotic motion in socially aware navigation, considering some aspects such as: type of robots used, characteristics, and type of sensors used to detect human behavioral cues, type of environment, and situations. Articles were collected on the ACM Digital Library, Emerald Insight, IEEE Xplore, ScienceDirect, MDPI, and SpringerLink databases. The PRISMA-ScR protocol was used to conduct the searches. Selected articles met the following inclusion criteria. They: (1) were published between January 2018 and August 2025, (2) were written in English, (3) were published in journals or conference proceedings, (4) focused on social robots, (5) addressed Socially Aware Navigation (SAN), and (6) involved the participation of volunteers in experiments. As a result, 22 studies were included; 77.27% of them employed mobile wheeled robots. Platforms using differential and omnidirectional drive systems were each used in 36.36% of the articles. 50% of the studies used a functional robot appearance, in contrast to bio-inspired appearances used in 31.80% of the cases. Among the frequency of sensors used to collect data from participants, vision-based technologies were the most used (with monocular cameras and 3D-vision systems each reported in 7 articles). Processing was mainly performed on board (50%) of the robot. A total of 59.1% of the studies were performed in real-world environments rather than simulations (36.36%), and a few studies were performed in hybrid environments (4.54%). Robot interactive behaviors were identified in different experiments: physical behaviors were present in all experiments. A few studies employed visual behaviors (2 times). In just over half of the studies (13 studies), participants were asked to provide post-experiment feedback. Full article