Search Results (51)

This research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of a six-degree-of-freedom bimanual apple-harvesting robot. Leveraging the kinematic architecture of the AUBO-i5 manipulator, three spatial layout configurations for dual-arm systems were evaluated, culminating in the adoption of a “workspace-overlapping Type B” arrangement. A functional prototype of the bimanual apple-harvesting system was subsequently fabricated. The study further involved developing control architectures for two end-effector types: a compliant gripper and a vacuum-based suction mechanism, with corresponding operational protocols established. A networked communication framework for parallel arm coordination was implemented via Ethernet switching technology, enabling both independent and synchronized bimanual operation. Additionally, an intersystem communication protocol was formulated to integrate the robotic vision system with the dual-arm control architecture, establishing a modular parallel execution model between visual perception and motion control modules. A coordinated bimanual harvesting strategy was formulated, incorporating real-time trajectory and pose monitoring of the manipulators. Kinematic simulations were executed to validate the feasibility of this strategy. Field evaluations in modern Red Fuji apple orchards assessed multidimensional harvesting performance, revealing 85.6% and 80% success rates for the suction and gripper-based arms, respectively. Single-fruit retrieval averaged 7.5 s per arm, yielding an overall system efficiency of 3.75 s per fruit. These findings advance the technological foundation for intelligent apple-harvesting systems, offering methodologies for the evolution of precision agronomic automation. Full article

Adopting mobile robots for high voltage (HV) live-line operations can mitigate personnel casualties and enhance operational efficiency. However, conventional mechanical arms cannot inspect concealed spaces in the power cable tunnel because their joint integrates metallic motors or hydraulic serial-drive mechanisms, which limit the arm’s length and insulation performance. Therefore, this study proposes a 7-degree-of-freedom (7-DOF) bionic mechanical arm with rigid-flexible coupling, mimicking human arm joints (shoulder, elbow, and wrist) designed for HV live-line operations in concealed cable tunnels. The arm employs a tendon-driven mechanism to remotely actuate joints, analogous to human musculoskeletal dynamics, thereby physically isolating conductive components (e.g., motors) from the mechanical arm. The arm’s structure utilizes dielectric materials and insulation-optimized geometries to reduce peak electric field intensity and increase creepage distance, achieving intrinsic self-insulation. Furthermore, the mechanical design addresses challenges posed by concealed spaces (e.g., shield tunnels and multi-circuit cable layouts) through the analysis of joint kinematics, drive mechanisms, and dielectric performance. The workspace of the proposed arm is an oblate ellipsoid with minor and major axes measuring 1.25 m and 1.65 m, respectively, covering the concealed space in the cable tunnel, while the arm’s quality is 4.7 kg. The maximum electric field intensity is 74.3 kV/m under 220 kV operating voltage. The field value is less than the air breakdown threshold. The proposed mechanical arm design significantly improves spatial adaptability, operational efficiency, and reliability in HV live-line inspection, offering theoretical and practical advancements for intelligent maintenance in cable tunnel environments. Full article

To address issues such as leaf occlusion, misalignment of the harvesting robotic arm, and limited harvesting range in hillside chili fields, this paper designs an intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling. The system integrates real-time data from a LiDAR and IMU inertial navigation system to reconstruct the chili point cloud occluded by leaves from multiple perspectives. To address issues such as misalignment of the robotic arm caused by terrain undulations, the system integrates an adaptive leveling platform and an H-shaped planar slide, combined with a gyroscope to dynamically adjust the arm’s posture in real time, ensuring arm stability while expanding its workspace. In addition, to ensure harvesting efficiency and pepper integrity, an integrated cutting–gripping flexible end effector is designed to achieve synchronized cutting and collection operations. The experiment shows that the system achieves recognition accuracy of 81.95% for occluded chili peppers and 89.04% for non-occluded chili peppers. The harvesting success rate is 86.33%, with a single harvesting operation taking 13.17 s. During prolonged operation, the harvesting success rate can be maintained at approximately 85.1%. In summary, the intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling provides a feasible solution for automated pepper harvesting. Full article

University libraries are essential academic spaces, yet existing smart systems often overlook user perception in environmental optimization. A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). Unlike prior works, our system fuses objective measurements and subjective input for personalized assessment. Environmental factors—including air quality, sound, temperature, humidity, and lighting—were monitored using microcontrollers and image processing. User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. KNNs achieved the highest F1 score (99.04%), validating the hybrid approach. A user interface analyzes environmental factors, identifying primary contributors to suboptimal conditions. A scalable fog–cloud architecture distributes computation between edge devices (fog) and cloud servers, optimizing resource management. Beyond libraries, the framework extends to other smart workspaces. By integrating the IoT, ML, and user-driven optimization, this study presents an adaptive decision support system, transforming libraries into intelligent, user-responsive environments. Full article

With the continuous development of modern robotics technology, in order to overcome the obstacles to the ability to complete tasks due to the fixed structure of the robot itself, to realize the reconfigurable purpose of the manipulator, it can be assembled into different degrees of freedom or configurations according to the needs of different tasks, which has the characteristics of a compact structure, high integrability, and low cost. The overall design scheme of a cable-free modular reconfigurable manipulator is proposed, and based on the target design parameters, the structural design of each module is completed, and the module library is constructed. Each module realizes rapid assembly or disassembly through a new type of docking mechanism module, which improves the flexibility and reliability of the manipulator. Meanwhile, a finite element analysis is carried out on the whole manipulator to optimize the structure that does not meet the strength and stiffness requirements. The wireless energy transmission module is integrated into the joint module to realize the cable-free design of the manipulator in the structure. The kinematic models of each module are established separately, providing a method to quickly construct the kinematics of different configurations of the manipulator, and the dexterity of the workspace is analyzed. Then, two methods, joint space planning and Cartesian space planning, are adopted to generate the corresponding motion paths and kinematic curves, which successfully verifies the reasonableness of the kinematics of the designed manipulator. Finally, combined with the results of the dynamics simulation, the corresponding dynamics curves of the end of each joint are generated to further verify the reliability of its design. It provides a new way of thinking for the research and development of highly intelligent and highly integrated manipulators. Full article

Current trends in the aerospace and UAV sectors emphasize integrating Artificial Intelligence (AI) technologies into the design process. AI technologies necessitate extensive data to capture the non-linearities in fluid phenomena. To address these needs, this work focuses on automating the data aggregation process for fixed-wing platforms, ranging from Micro–Mini to HALE-Strike UAVs, as classified by NATO. Specifically, this paper presents a framework for automating the tedious tasks required for geometry generation, mesh generation, and solution setup in a commercial Computational Fluid Dynamics (CFD) solver, for any arbitrary wing within the aforementioned design space. By combining various well-established open-source suites and commercial software via Python scripting, the preprocessing steps up to the solution require only a few minutes on a typical laptop workspace. Despite the rapid geometry acquisition, mesh generation, and solution setup through the pipeline, the guidelines and common practices for subsonic external flow simulations are still strictly followed. This results in solutions with a deviation of merely sub 5% from those of an experienced designer, even for the extremes of the flight envelope. The proposed framework significantly reduces design iteration times, enabling more efficient and innovative UAV development. Additionally, the framework’s ability to accumulate high-quality data for machine learning enhances predictive modeling and optimization capabilities across UAV design practices. Full article

Background/Objectives: Consciousness is a complex and elusive phenomenon encompassing self-awareness, sensory perception, emotions, and cognition. Despite significant advances in neuroscience, understanding the neural mechanisms underlying consciousness remains challenging. Pain, as a subjective and multifaceted experience, offers a unique lens for exploring consciousness by integrating sensory inputs with emotional and cognitive dimensions. This study examines the relationship between consciousness and pain, highlighting the potential of pain as a model for understanding the interplay between subjective experience and neural activity. Methods: Literature review. Results: Key theories of consciousness, such as the Global Workspace Theory and the Integrated Information Theory, provide diverse frameworks for interpreting the emergence of consciousness. Similarly, pain research emphasizes the role of subjective interpretation and emotional context in shaping sensory experiences, reflecting broader challenges in consciousness studies. The limitations of current methodologies, particularly the difficulty of objectively measuring subjective phenomena, like pain and consciousness, are also addressed. This highlights the importance of neural correlates, with a particular focus on brain regions, such as the anterior cingulate cortex and the insula, which bridge sensory and emotional experiences. By analyzing the shared attributes of pain and consciousness, this study underscores the potential for pain to serve as a measurable proxy in consciousness research. Conclusions: Ultimately, it contributes to unraveling the neural and philosophical underpinnings of consciousness, offering implications for mental health treatment and advancements in artificial intelligence. This study fills a critical gap by leveraging pain as a measurable and reproducible model for exploring the neural and subjective mechanisms of consciousness. By combining theoretical frameworks with empirical evidence, it offers novel insights into how consciousness emerges from neural processes. Full article

An automated micro-tweezers system with a flexible workspace would benefit the intelligent sorting of live cells. Such micro-tweezers could employ a forced vortex strong enough to capture a single cell. Furthermore, addressable control of the position to the vortex would constitute a robotic system. In this study, a spherical micro-object composed of super paramagnetic particles tightly packed in a non-magnetic resin is rotated with a combined magnetic field of permanent magnets. The said magnetic field is articulated by an open-kinematic chain controlled with a simple adaptive PI-control scheme. A vortex is formed as the spherical particle, assumed to be submerged under the surface of fluid, and follows the position and orientation of the external magnetic field. This forced vortex induces a radial pressure gradient that captures the live cell orbiting around the spherical object combined with the inertial effects. Here, a comprehensive mathematical model is presented to reflect on the dynamics of such micro-tweezer systems. Numerical results demonstrate that it is theoretically possible to capture and tow a bacterium cell while meeting extreme tracking references for motion control. Magnetic and fluid forces on the spherical particle traverse the vortex and the bacterium cell, with orbiting and sporadic collusion of the bacterium cell around the spherical particle, and the positions of the end-effector, i.e., the magnets, are analyzed. Full article

This paper describes the task of controlling a robot to enable energy savings in the case of one- or two-axis failure. The proposed algorithms are tested in a task similar to “pick and place” but without gripping. Obstacles are present in the robot’s workspace. The goal of the algorithm is to control the robot in such a way that energy consumption is minimized while also avoiding obstacles and ensuring fault tolerance in the event of axis failures. The algorithm uses a developed torque model of the robot, which is employed to calculate the energy requirements for each possible movement step in the robot’s position. In the robot’s control system, artificial intelligence methods are also applied. Specifically, a genetic algorithm is used to generate learning data for the selection of the optimal kinematic configuration of the robot, and a multilayer perceptron is utilized to predict the parameters of the defined reward function. This function is crucial for selecting the optimal action at each time step. The study demonstrates that the application of the algorithm leads to a reduction in robot energy consumption. Studies conducted in simulation and verified on a real robot for 10 different obstacle and target positions and 22 possible kinematic configurations of the robot, consisting of all axes active, any one axis inactive, or any two axes inactive, confirm the energy-saving possibilities. Full article

Continuous robots have attracted more and more attention from the robotics community due to their high degree of flexibility and pliability, and have shown great potential for application in a variety of fields. With the continuous progress of material science, control technology, and artificial intelligence, the performance and application range of soft robotics have been further expanded, in which the cable drive has the advantages of large workspace, high flexibility, etc. The cable-driven soft robotic arm serves as an ultra-redundant robot that can operate in cramped and confined environments. In this paper, a cable-driven soft robot based on soft continuums and a cross gimbal is presented. The kinematics of the cable-driven soft robot is modeled and the mapping relations of the kinematics are solved by the D–H method and piecewise constant curvature, and the relations between the cable length, joint angle, and pose are further derived. Finally, the motion space of the cable-driven soft robot in the three-dimensional coordinate system is obtained by MATLAB2021b, and the single-segment soft body is simulated and analyzed using ADAMS to compare the theoretical data with the actual data and verify the reliability of this structure and method. Full article

With the rapid rise in technological advancements, health can be tracked and monitored in multiple ways. Tracking and monitoring healthcare gives the option to give precise interventions to people, enabling them to focus more on healthier lifestyles by minimising health issues concerning long screen time. Artificial Intelligence (AI) techniques like the Large Language Model (LLM) technology enable intelligent smart assistants to be used on mobile devices and in other cases. The proposed system uses the power of IoT and LLMs to create a virtual personal assistant for long-time screen users by monitoring their health parameters, with various sensors for the real-time monitoring of seating posture, heartbeat, stress levels, and the motion tracking of eye movements, etc., to constantly track, give necessary advice, and make sure that their vitals are as expected and within the safety parameters. The intelligent system combines the power of AI and Natural Language Processing (NLP) to build a virtual assistant embedded into the screens of mobile devices, laptops, desktops, and other screen devices, which employees across various workspaces use. The intelligent screen, with the integration of multiple sensors, tracks and monitors the users’ vitals along with various other necessary health parameters, and alerts them to take breaks, have water, and refresh, ensuring that the users stay healthy while using the system for work. These systems also suggest necessary exercises for the eyes, head, and other body parts. The proposed smart system is supported by user recognition to identify the current user and suggest advisory actions accordingly. The system also adapts and ensures that the users enjoy proper relaxation and focus when using the system, providing a flexible and personalised experience. The intelligent screen system monitors and improves the health of employees who have to work for a long time, thereby enhancing the productivity and concentration of employees in various organisations. Full article

Due to the complex growth positions of dragon fruit and the difficulty in robotic picking, this paper proposes a six degrees of freedom dragon fruit picking robot and investigates the manipulator’s motion characteristics to address the adaptive motion issues of the picking manipulator. Based on the agronomic characteristics of dragon fruit cultivation, the structural design of the robot and the dimensions of its manipulator were determined. A kinematic model of the dragon fruit picking robot based on screw theory was established, and the workspace of the manipulator was analyzed using the Monte Carlo method. Furthermore, a dynamic model of the manipulator based on the Kane equation was constructed. Performance experiments under trajectory and non-trajectory planning showed that trajectory planning significantly reduced power consumption and peak torque. Specifically, Joint 3’s power consumption decreased by 62.28%, and during the picking, placing, and resetting stages, the peak torque of Joint 4 under trajectory planning was 10.14 N·m, 12.57 N·m, and 16.85 N·m, respectively, compared to 12.31 N·m, 15.69 N·m, and 22.13 N·m under non-trajectory planning. This indicated that the manipulator operates with less impact and smoother motion under trajectory planning. Comparing the dynamic model simulation and actual testing, the maximum absolute error in the joint torques was −2.76 N·m, verifying the correctness of the dynamic equations. Through field picking experiments, it was verified that the machine’s picking success rate was 66.25%, with an average picking time of 42.4 s per dragon fruit. The manipulator operated smoothly during each picking process. In the study, the dragon fruit picking manipulator exhibited good stability, providing the theoretical foundation and technical support for intelligent dragon fruit picking. Full article

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot’s base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method’s adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection. Full article

Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms. Full article

With the surging interest in electric vehicles (EVs), there is a need for advancements in the development and dismantling of lithium-ion batteries (LIBs), which are highly important for the circular economy. This paper introduces an intelligent hybrid task planner designed for multi-robot disassembly and demonstrates its application to an EV lithium-ion battery pack. The objective is to enable multiple robots to operate collaboratively in a single workspace to execute battery disassembly tasks efficiently and without collisions. This approach can be generalized to almost any disassembly task. The planner uses logical and hierarchical strategies to identify object locations from data captured by cameras mounted on each robot’s end-effector, orchestrating coordinated pick-and-place operations. The efficacy of this task planner was assessed through simulations with three trajectory-planning algorithms: RRT, RRTConnect, and RRTStar. Performance evaluations focused on completion times for battery disassembly tasks. The results showed that completion times were similar across the planners, with 543.06 s for RRT, 541.89 s for RRTConnect, and 547.27 s for RRTStar, illustrating that the effectiveness of the task planner is independent of the specific joint-trajectory-planning algorithm used. This demonstrates the planner’s capability to effectively manage multi-robot disassembly operations. Full article