Search Results (226)

Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands. This study presents the design of a tendon-driven bionic spherical robot wrist, utilizing two pairs of cables that mimic antagonist muscle pairs. The cables are actuated by pulleys driven by servo motors, allowing for two primary wrist motions: flexion–extension and ulnar–radial deviation. The performance Please confirm if the “1583 Iiyama” is necessary. Same as belowof the proposed robot wrist is validated through manipulation experiments using a prototype, demonstrating its capability to achieve a full range of motion for both ulnar and radial deviation. This wrist mechanism is expected to be integrated into robotic systems, enabling greater flexibility and more human-like movement capabilities. Full article

Snake robots, inspired by the biology of snakes, are bionic robots with multiple degrees of freedom and strong robustness. These robots represent a current area of significant research interest within the field of robotics. Snake robots have a wide range of applications in many fields, advancing the integration of bionics, robotics, and cybernetics, while playing a crucial role in performing survey and rescue missions. This survey presents the latest technological advancements in modeling, motion control, and guidance laws for planar snake robots, and provides a unified perspective based on the existing results. To achieve target-tracking control of robots in complex environments, we present a feasible approach that integrates guided vector field technology and transforms the target-tracking and obstacle avoidance problem into a reference angle tracking issue. Finally, this paper analyzes and summarizes the development process and key technologies of snake robot control and provides an outlook on future development trends. Full article

The application of smart materials in robots has attracted considerable research attention. This study developed an inchworm robot that integrates smart materials and a bionic design, using the unique properties of magnetorheological elastomers (MREs) to improve the performance of robots in complex environments, as well as their adaptability and movement efficiency. This research stems from solving the problem of the insufficient adaptability of traditional bionic robots on different surfaces. A robot that combines an MRE foot, an electromagnetic control system, and a bionic motion mechanism was designed and manufactured. The MRE foot was made from silicone rubber mixed with carbonyl iron particles at a specific ratio. Systematic experiments were conducted on three typical surfaces, PMMA, wood, and copper plates, to test the friction characteristics and motion performance of the robot. On all tested surfaces, the friction force of the MRE foot was reduced significantly after applying a magnetic field. For example, on the PMMA surface, the friction force of the front leg dropped from 2.09 N to 1.90 N, and that of the hind leg decreased from 3.34 N to 1.75 N. The robot movement speed increased by 1.79, 1.76, and 1.13 times on PMMA, wooden, and copper plate surfaces, respectively. The MRE-based intelligent foot design improved the environmental adaptability and movement efficiency of the inchworm robot significantly, providing new ideas for the application of intelligent materials in the field of bionic robots and solutions to movement challenges in complex environments. Full article

The rapid advancement of tactile electronic skin (E-skin) has highlighted the effectiveness of incorporating bionic, force-sensitive microstructures in order to enhance sensing performance. Among these, cilia-like microstructures with high aspect ratios, whose inspiration is mammalian hair and the lateral line system of fish, have attracted significant attention for their unique ability to enable E-skin to detect weak signals, even in extreme conditions. Herein, this review critically examines recent progress in the development of cilia-inspired bionic tactile E-skin, with a focus on columnar, conical and filiform microstructures, as well as their fabrication strategies, including template-based and template-free methods. The relationship between sensing performance and fabrication approaches is thoroughly analyzed, offering a framework for optimizing sensitivity and resilience. We also explore the applications of these systems across various fields, such as medical diagnostics, motion detection, human–machine interfaces, dexterous robotics, near-field communication, and perceptual decoupling systems. Finally, we provide insights into the pathways toward industrializing cilia-inspired bionic tactile E-skin, aiming to drive innovation and unlock the technology’s potential for future applications. Full article

Electronic skin is widely employed in multiple applications such as health monitoring, robot tactile perception, and bionic prosthetics. In this study, we fabricated millimeter-scale electronic skin featuring compact sensing units using the Boston Micro Fabrication S130 (a high-precision additive manufacturing device) and the template removal method. We used a gallium-based liquid metal and achieved an inner channel diameter of 0.1 mm. The size of the sensing unit was 3 × 3 mm². This unit exhibited a wide linear sensing range (10–22,000 Pa) and high-pressure resolution (10 Pa) even on an ultra-curved surface (radius of curvature was 6 mm). Sliding was successfully detected at speeds of 8–54 mm/s. An artificial nose with nine sensing units was fabricated, and it exhibited excellent multitouch and sliding trajectory recognition capabilities. This confirmed that the electronic skin functioned normally, even on an ultra-curved surface. Full article

Direct-drive servo systems are extensively applied in biomimetic robotics and other bionic applications, but their performance is susceptible to uncertainties and disturbances. This paper proposes an adaptive disturbance rejection Zeta-backstepping control scheme with adjustable damping ratios to enhance system robustness and precision. An iron-core permanent magnet linear synchronous motor (PMLSM) was employed as the experimental platform for the development of a dynamic model that incorporates compensation for friction and cogging forces. To address model parameter uncertainties, an indirect parameter adaptation strategy based on a recursive least squares algorithm was introduced. It updates parameters based on the system state instead of output error, ensuring robust parameter convergence. An integral sliding mode observer (ISMO) was constructed to estimate and compensate for residual uncertainties, achieving finite-time state estimation. The proposed Zeta-backstepping controller enables adjustable damping ratios through parameterized control laws, offering flexibility in achieving desired dynamic performance. System stability and bounded tracking performance were validated via a second-order Lyapunov function analysis. Experimental results on a real PMLSM platform demonstrated that, while achieving adjustable damping ratio dynamic characteristics, there is a significant improvement in tracking accuracy and disturbance suppression. This underscores the scheme’s potential for advancing precision control in biomimetic robotics and other direct-drive system applications. Full article

Quadruped robots, with their biomimetic structure, are capable of stable locomotion in complex terrains and are vital in rescue, exploration, and military applications. However, developing multi-modal robots that feature simple motion control while adapting to diverse amphibious environments remains a significant challenge. These robots need to excel at obstacle-crossing, waterproofing, and maintaining stability across various locomotion modes. To address these challenges, this paper introduces a novel leg–fin integrated propulsion mechanism for a bionic quadruped robot, utilizing rapidly advancing soft materials and integrated molding technologies. The robot’s motion is modeled and decomposed using an improved central pattern generator (CPG) control network. By leveraging the control characteristics of the CPG model, global control of the single-degree-of-freedom drive mechanism is achieved, allowing smooth transitions between different motion modes. The design is verified through simulations conducted in the Webots environment. Finally, a physical prototype of the quadruped compliant robot is constructed, and experiments are carried out to test its walking, turning, and obstacle-crossing abilities in various environments. The experimental results demonstrate that the robot shows a significant speed advantage in regions where land and water meet, reaching a maximum speed of 1.03 body lengths per second (bl/s). Full article

This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg schedule patterns for the complex configuration of the Pegasus, the requirement for various gaits, and the design of reward functions for MA-RL agents. Agents were trained using two variations of policy networks based on the framework, and real-world tests show promising results with easy policy transfer from simulation to the actual hardware. The proposed framework models acquired higher rewards and converged faster in training than other variants. Various experiments on the robot deployed framework showed fast response (0.8 s) under disturbance and low linear, angular velocity, and heading error, which was 2.5 cm/s, 0.06 rad/s, and 4°, respectively. Overall, the study demonstrates the feasibility of the proposed MA-RL control framework. Full article

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption. Full article

To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRBO-DWA. This algorithm is specifically applied to plan the path for a tube-changing robot in a knitting workshop. The process begins with spatial modeling based on the actual parameters of the workshop, followed by the development of a comprehensive, objective function for the robot in line with the relevant constraints. The NRBO algorithm is then integrated with the DWA algorithm to boost its dynamic obstacle avoidance capabilities, while a path correction mechanism is introduced to minimize unnecessary detours. Finally, a comparative experiment is designed to evaluate the algorithm against the GA, PSO, and SSA algorithms. Simulation results demonstrate that in a dynamically complex 3D environment, the NRBO-DWA algorithm outperforms in terms of higher 3D search efficiency, shorter total path length, and faster planning times. Full article

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method’s robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference. Full article

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. Full article

Thermal management presents a significant challenge in electric design, particularly in densely packed electronic systems. This study proposes a theoretical model for radiative bionic skin that emulates human skin, enabling the self-adaptive modulation of the thermal exhaustion rate to maintain homeostasis for objects covered by the skin in fluctuating thermal environments. The proposed artificial skin consists of phase change material (VO₂) nanoparticles embedded in a low-loss matrix situated on a metallic substrate with a minimal thickness of several micrometers. The findings from our theoretical analyses indicate that substantial alterations in thermal radiation power around the phase transition temperature of 340 K enable a silicone substrate to sustain a relatively stable temperature, with variations confined to ±6 K, despite external heat fluxes ranging from 150 to 450 W/m². Furthermore, to improve the spectral resemblance to natural skin, a plasmonic surface composed of self-assembled silver nanocubes is incorporated, allowing for modifications to the visible light properties of the bionic skin while maintaining its infrared characteristics. This theoretical investigation offers a cost-effective and conformal approach to the design of ultra-compact, fully passive, and versatile thermal management solutions for robotic systems and related technologies. Full article

Spiders have unique biological characteristics and excellent maneuverability, making them an ideal model for bionic robot design. However, traditional bionic spider robot designs usually have multiple degrees of freedom and confront many challenges. These challenges include complex control requirements, higher energy consumption, larger size and weight, higher risk of failure, and higher cost. This study proposes a leg design with two degrees of freedom to reduce its control and manufacturing costs. It can better control leg movement and improve leg force through a multi-link mechanism and a dual-motor system. In addition, the triangular gait and hexagonal body structure align the weight of the body with the support point, thereby enhancing stability. This study offers a comprehensive and organized approach to bio-inspired robot design, providing a valuable reference for future bionic robot development. Full article

An exoskeleton is a wearable device with human–machine interaction characteristics. An ideal exoskeleton should have kinematic and kinetic characteristics similar to those of the wearer. Most traditional exoskeletons are driven by rigid actuators based on joint torque or position control algorithms. In order to achieve better human–robot interaction, flexible actuators have been introduced into exoskeletons. However, exoskeletons with fixed stiffness cannot adapt to changing stiffness requirements during assistance. In order to achieve collaborative control of stiffness and torque, a bionic variable stiffness hip joint exoskeleton (BVS-HJE) is designed in this article. The exoskeleton proposed in this article is inspired by the muscles that come in agonist–antagonist pairs, whose actuators are arranged in an antagonistic form on both sides of the hip joint. Compared with other exoskeletons, it has antagonistic actuators with variable stiffness mechanisms, which allow the stiffness control of the exoskeleton joint independent of force (or position) control. A BVS-HJE model was established to study its variable stiffness and static characteristics. Based on the characteristics of the BVS-HJE, a control strategy is proposed that can achieve independent adjustment of joint torque and joint stiffness. In addition, the variable stiffness mechanism can estimate the output force based on the established mathematical model through an encoder, thus eliminating the additional force sensors in the control process. Finally, the variable stiffness properties of the actuator and the controllability of joint stiffness and joint torque were verified through experiments. Full article