Search Results (2,328)

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies. Full article

Industrial robotic arms are fundamental components of modern automated production lines, executing critical tasks such as welding, painting, and assembly. Such high-precision operations often require careful manual tool positioning during the initial setup. To automate and refine this process, a highly accurate kinematic model of the robot is essential. In this paper, the authors propose a novel algorithm for kinematic parameter calibration using photogrammetry to track multiple robot links simultaneously. The proposed multi-link calibration approach provides a more precise parameter estimation and introduces the practical possibility of continuous parameter refinement while the robot executes its primary operational tasks. The superior accuracy and robustness of the proposed methodology are confirmed through comprehensive simulation experiments, and the feasibility of the approach is successfully demonstrated on a real robotic arm. Full article

This paper presents a mathematical modeling-based vision-guided triple-loop control method for lane tracking of an underactuated bicycle robot. To describe the coupling between lateral balance and path tracking, a reaction-wheel-based inverted-pendulum model is established using the Lagrange formulation. Based on the linearized dynamics, the transfer function between the flywheel rotational speed and the motor torque is derived, providing a mathematical basis for designing the gain-scheduled triple-loop PID controller. To generate continuous control inputs under practical visual disturbances, an improved Hough transform, a near-field multi-layer sliding window detector, and a multi-scenario finite-state-machine strategy are incorporated for lateral deviation estimation and path reconstruction. A cascaded smoothing filter is further introduced to reduce high-frequency command fluctuations and improve the closed-loop control response. Real-vehicle experiments on an STM32F407-based underactuated bicycle robot demonstrate that the proposed framework achieves stable dynamic balance and robust lane tracking. Compared with a conventional Hough-transform and sliding window method, the lateral RMSE is reduced by 40.2%, 39.85%, and 32.35% in straight, left-turn, and right-turn scenarios, respectively. Full article

Autonomous navigation in agricultural environments remains a key challenge for the deployment of mobile robots in precision viticulture. In this paper, we present the numerical and experimental validation of a LiDAR–inertial navigation and mapping framework for mobile robots operating in vineyard-like scenarios. A realistic vineyard simulation environment reproducing the geometric structure of vine rows is first developed to evaluate the performance of the proposed framework, considering multiple metrics including mapping time, speed stability, path tracking error, and point cloud reconstruction density. Then, the proposed approach is tested in a real vineyard using a Scout 2.0 mobile robot. Numerical and experimental results demonstrate the feasibility of the navigation and mapping strategy and its robustness during extensive repeated tests in the field. Full article

Proactive robotic assistance in human–robot collaboration (HRC) requires systems that can perceive evolving task contexts, anticipate user needs, and intervene appropriately without disrupting human workflow. We present the Agentic Unified Robotic Assistance (AURA) Framework, which couples Large Language Model (LLM) reasoning grounded by Standard Operating Procedures (SOPs) with a modular layer of specialized Intent, Motion, Perception, Sound, Affordance, and Performance Monitors that supply structured context to a central decision-making module, making the framework reconfigurable and auditable without retraining or re-prompting. We introduce a human-in-the-loop teleoperation data collection methodology and an offline evaluation scheme with an Appropriateness Score (A-Score) tailored to proactive intervention timing, and release a benchmark dataset of annotated multimodal HRC episodes containing workspace and robot wrist camera videos, robot joint states, and labeled intervention events. Across three tasks of varying complexity, we observe progressive gains in intent prediction and decision-making as the modules are supplied with richer grounded context (prior-state memory and tracked object locations), with Combined F1 rising by over 20 points between context-poor and context-rich conditions. The structured grounding allows lightweight multimodal backbones such as Gemini 3.1 Flash Lite to perform on par with heavier reasoning-tier models at roughly one-fifth the inference latency. Together, these contributions establish a scalable framework, benchmark, and evaluation methodology for advancing proactive robotic assistance in collaborative environments. Full article

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured injection of neural network correction directly into the physical drive model with a controlled Lyapunov-based adaptation constraint. A mathematical model of the electromechanical drive of a differential mobile platform is developed, taking into account electrical and mechanical dynamics, wheel-to-surface contact interaction, and the system’s energy characteristics. Numerical simulation results demonstrate that the hybrid approach improves tracking accuracy, improves transient response, and ensures stable operation of the control system under parametric uncertainty, adhesion changes, and external disturbances. The proposed architecture maintains the physical interpretability of the model while simultaneously enhancing the system’s adaptability. The obtained results confirm the effectiveness of the developed method and its potential for application in control systems for mobile robotic platforms. Full article

When operating in complex and obstacle-dense environments, micro UAV swarms often face severe cooperative positioning failures due to transient non-line-of-sight (NLOS) interference and cascaded inertial sensor drift. To address this, this work proposes a fault-tolerant positioning framework integrating multi-agent deep reinforcement learning with cooperative extended Kalman filtering (MADRL-CEKF). The system incorporates a link-level dynamic soft isolation mechanism that dynamically adjusts observation covariance to effectively sever paths of cooperative error contagion. An adaptive Markov smoothing constraint is mathematically embedded to mitigate high-frequency control jitter typical of AI-driven policies. Crucially, the framework implements a resource-aware multi-objective reward architecture tailored for micro UAVs. Evaluated through high-fidelity simulations and offline physical datasets, the proposed framework achieves a 96.01% reduction in average tracking error (RMSE) under extreme multi-node cascaded failures, completely preventing system divergence. Furthermore, through autonomous multi-objective trade-offs, the system reduces processing delay by 44% (to 25.1 ms) and computational energy consumption by 41% with only a marginal accuracy compromise of 0.16 m, strictly keeping the execution time within the 50 ms real-time threshold. The MADRL-CEKF framework effectively bridges the gap between sophisticated AI decision-making and strict engineering constraints, providing a highly robust and resource-efficient navigation paradigm for swarm robotics. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

The contact force between the ultrasound probe and human skin directly affects image quality, patient safety, and comfort. In infant developmental dysplasia of the hip (DDH) ultrasound examinations, higher force control precision is required, as infants have thin skin and soft cartilage that are easily deformed under excessive probe pressure. This paper proposes a comprehensive force control method for DDH ultrasound robots. Firstly, an online gravity calibration approach is employed to estimate the installation tilt, sensor zero offset, and probe center of gravity, thereby improving force measurement accuracy. Then, a torque-based pose control algorithm is adopted to achieve conformal probe–skin contact. Finally, a variable admittance control strategy based on fuzzy neural network (FNN) is proposed, which adaptively regulates the damping coefficient based on the force error and its rate, enabling stable force control without explicit soft-tissue modeling. Experiments on an infant phantom and human skin show that the proposed method achieves force fluctuation amplitudes of 0.0984 ± 0.0012 N and 0.0976 ± 0.0014 N, respectively, with absolute steady-state force errors below 0.01 N. Compared with conventional admittance control, it significantly reduces force oscillations and improves tracking accuracy. In infant experiments, the method enables smooth convergence to the desired force and maintains relatively stable probe–skin interaction, which contributes to consistent ultrasound image acquisition and reduces tissue deformation. These results suggest that the proposed method can provide a feasible force control basis for stable and gentle robotic DDH ultrasound scanning. Full article

Pneumatic pouch motors are soft actuators that contract when inflated. Their low cost and ease of fabrication make them ideal choices for disposable systems such as those used in robotic surgery. However, the dynamics of pouch motors are highly nonlinear, which complicates control. In this paper, we investigate the position control of an antagonistic pair of soft pneumatic pouch motors. An analytical model of system dynamics, including the pressure dynamics in the pouches, is proposed. To compensate for uncertainties and disturbances, a nonlinear observer is constructed based on the Immersion and Invariance methodology. A new model-based nonlinear controller, constructed using a nested sliding variable, is designed for tracking tasks. Stability conditions are discussed, and the effectiveness of the new controller is demonstrated in simulations and experiments. The new controller is compared with a reduced-order version that neglects pressure dynamics. The results indicate that both controllers are effective in tracking tasks, with the new controller showing improved accuracy by up to 33.3% in experiments. Full article

This paper introduces the research progress of path planning, trajectory tracking control, and multi-machine collaborative operation systems for agricultural robots. It summarizes the development laws of 3D terrain modeling and adaptive path planning algorithms for complex agricultural environments such as hills and mountains, and analyzes the dynamic disturbance characteristics of agricultural machinery under slip, sideslip, and dynamic load changes. Through comprehensive analysis, it is found that traditional kinematic control models have limitations in complex and unstructured environments. Combining soil mechanics mechanisms, variable load identification, and robust control strategies is key to improving trajectory tracking stability and operational quality. In terms of multi-machine collaboration, this paper discusses master–slave collaboration, distributed control, and task allocation modes. It further identifies that the stability of collaboration and interoperability standards between devices in weak network environments are currently the main bottlenecks limiting the large-scale application of this technology. Finally, this paper provides prospects for future research directions and suggests strengthening the closed-loop integration of perception, decision-making, and dynamic models, establishing industry unified standards, and enhancing the safety of the entire lifecycle of operations, providing suggestions for the unmanned application of agricultural robots. Full article

Efficient irrigation management requires reliable information on soil water content, yet low-cost capacitive sensors often lack proper calibration. This study evaluates the metrological performance of two DF Robot probes, SEN0193 (S1) and SEN0308 (S3), under controlled variations in porous media properties. Glass beads of three size classes (<50 µm, 70–110 µm, and 400–600 µm) were used to simulate fine, medium, and coarse textures. Sensors were tested at four water contents (0, 10, 20, and 30%) and four salinity levels (0, 4, 8, and 16 g NaCl L⁻¹). Results show that the manufacturer-recommended air/water calibration is unsuitable for soils or porous media; calibration should instead be performed under dry and saturated conditions specific to the medium. S1 exhibited stable and homogeneous responses, with intra-unit CV ≤ 2%, but moderate calibration accuracy (R² = 0.68–0.80; RMSE = 8.9–12.9% VWC across textures). S3 showed a wider signal range (80–90% larger than S1), better fit in coarse texture (R² = 0.96; RMSE = 3.5% VWC), but higher unit-to-unit variability (CV = 6–14%) and performance degradation in fine and saline media. Although these sensors cannot provide accurate absolute quantification, their ability to track moisture trends makes them useful for irrigation management, provided calibration accounts for medium texture and salinity. Full article

A pipeline intelligent plugging robot (PIPR) is an important tool in subsea pipeline maintenance and emergency repair. Precise position-tracking control is crucial for the in-pipe plugging operation of a PIPR. The anchoring module is the key component responsible for fixed-point braking, which faces the challenges of insufficient structural adaptability within a narrow space. Additionally, traditional PID control may lead to poor robustness under fluctuating working conditions and load disturbances. To address these issues, this study designs a novel anchoring module combining screw transmission, an eccentric crank–slider mechanism, and a parallelogram linkage. To achieve adaptive tracking control, the improved deep deterministic policy gradient (DDPG) algorithm is introduced to optimize the parameters of the PID controller. A reward function with mechanical constraint penalties and a dual-phase strategy is proposed for dynamic parameter optimization. All control performances are analyzed and verified through simulations. The results indicate that the proposed method outperforms traditional PID control as regards response speed, overshoot, and robustness, which can achieve precise anchoring. This study provides a theoretical foundation for ensuring the precision of the plugging process. Full article

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article