Search Results (713)

Gas-insulated substations (GISs) have been widely adopted in modern power systems due to their compact design and high reliability. However, the potential generation of toxic byproducts poses significant risks to manual operations such as gas pressure adjustments, highlighting the necessity for robotic deployments. This paper proposes a Robot Operating System (ROS)-based robotic framework with human–robot collaborative autonomy for GIS operations. The framework employs a 6-degree-of-freedom (6-DOF) robotic arm, integrated with a motion control system for trajectory execution, a visual perception system enhanced by the coordinate attention (CA) mechanism for small-scale detection and localization, and a communication system for data exchange. The framework improves the accuracy of component perception and enables fine manipulation in complex environments, reducing reliance on manual intervention while facilitating a safe collaborative autonomous workflow through dynamic adjustment of autonomy level and control authority. Experimental results on the representative gas pressure adjustment task demonstrate an autonomous operational success rate exceeding 90% under the proposed configuration in dynamic scenarios. By enhancing safety and precision, this study advances robotic solutions for hazardous operations and lays a foundation for broader infrastructure applications. Full article

This paper proposes a hierarchical control architecture for trajectory tracking of differential-drive nonholonomic mobile robots intended for the Very Small Size Soccer (VSSS) competition. The proposed framework integrates a high-level nonlinear trajectory-tracking controller, which computes the desired linear and angular velocity references based on the robot pose and prescribed trajectory, with a low-level Linear Quadratic Gaussian (LQG) controller responsible for the independent velocity regulation of the drive motors. The LQG controller combines optimal state feedback through a Linear Quadratic Regulator (LQR) with integral action and state estimation provided by a Kalman filter, enabling accurate velocity regulation in the presence of modeling uncertainties and measurement noise. The complete architecture is implemented on an embedded differential-drive robotic platform equipped with incremental encoders, vision-based localization, and an ESP32-based controller, and is validated exclusively through real-time experimental tests under practical operating conditions. The experimental evaluation includes wheel velocity step-response tests and circular and lemniscate trajectory tracking under both forward and reverse motion. The results demonstrate accurate wheel velocity regulation, effective attenuation of encoder measurement noise, consistent tracking of the prescribed trajectories, and experimentally measurable trade-offs between spatial tracking accuracy, wheel velocity regulation, and control effort under different trajectory geometries and motion directions. Overall, the proposed architecture provides a modular hierarchical framework that integrates nonlinear high-level trajectory tracking with optimal low-level velocity regulation for real-time autonomous mobile robot applications. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

Reliable visual perception is a prerequisite for autonomous cable duct inspection, particularly for recognizing pipe-joint dislocations and obstruction-related hazards. Images acquired inside cable ducts are often affected by restricted viewpoints, uneven illumination, wall-texture interference, partial occlusion, and substantial variations in target geometry and scale. These factors increase the likelihood of missed targets, false alarms, and inaccurate bounding boxes. This study develops DPR-YOLOv9 from the YOLOv9c detector, where DPR represents deformable-strip feature extraction, position-aware attention, and regression optimization. In the backbone, a Deformable Strip Convolution Network (DSCN) adjusts its sampling pattern to better describe elongated boundaries, displaced joints, and irregular obstacle contours. CoordAttention is introduced into the multi-scale fusion path to retain directional coordinate cues and emphasize spatially relevant features. In addition, Inner-IoU modifies the regression constraint through auxiliary boxes, providing more effective optimization for small or partially occluded targets. Across three independent runs, DPR-YOLOv9 achieved mean Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.944, 0.933, 0.940, and 0.751, respectively, while maintaining an inference speed of 67.85 FPS. The results indicate that the proposed detector improves both recognition reliability and localization quality for robotic cable duct inspection. Full article

Bridging the real-to-sim gap in low-cost autonomous mobile robotics requires careful cross-domain alignment of kinematic geometry, actuator behavior, and sensor characteristics. This paper presents a systematic Real-to-Sim parameter calibration and multi-stage experimental validation framework for a low-cost differential-drive unmanned ground vehicle (Jackson UGV) operating within NVIDIA Isaac Sim. The calibration process distinguishes initial product/design references, directly measured physical geometry, empirically adjusted ROS 2 runtime parameters, and simulation-specific PhysX parameters. By tuning virtual wheel geometry, inertial sensor profiles, and PhysX joint-drive damping, the proposed framework enables controlled comparison between physical execution and digital-twin behavior. Benchmark evaluations across three experimental stages—square waypoint-tracking trajectories, continuous figure-eight maneuvers, and dynamic obstacle avoidance in a mapped maze course—quantify rotational repeatability, temporal alignment, estimator consistency, and cross-domain trajectory deviation. The square and figure-eight trials reveal a proprioceptive “estimator optimism gap” in which onboard EKF estimates remain internally repeatable while underestimating terminal displacement relative to external floor measurements or simulator-provided reference poses. In Stage 3, 2D LiDAR-based localization and Nav2/DWB local planning reduce dependence on purely proprioceptive dead reckoning, achieving $100\%$ goal completion without observed collision events across both physical and virtual deployments. The results support the calibrated digital twin as a controlled simulation baseline for studying cross-domain navigation behavior and for future sim-to-real evaluation of autonomous mobile robot navigation algorithms. Full article

Reliable localization and mapping are critical for intelligent robotic systems operating in dynamic indoor environments, where pedestrians and other moving objects can lead to erroneous feature associations, map contamination, and accumulated trajectory drift. To address these challenges, this study proposes the Boundary-Protected Semantic-Geometric Dynamic-Probability (Boundary-SGDP) framework, an enhanced red–green–blue-depth (RGB-D) visual simultaneous localization and mapping (SLAM) system based on boundary-protected semantic–geometric dynamic-probability estimation. The proposed method combines instance-level semantic priors generated by the YOLO26n-seg detector, a segmentation-oriented model in the You Only Look Once (YOLO) family, and the Segment Anything Model 2 (SAM2) with morphological region decomposition and RGB-D depth-edge detection. Potentially dynamic regions are further divided into dynamic interiors, semantic boundary protection bands, and geometrically informative depth-edge regions. Semantic and geometric cues are integrated to estimate a dynamic score for each feature, which is subsequently propagated to the MapPoint level as a dynamic probability. During pose optimization, these probabilities are used to adaptively adjust the weights of reprojection constraints, thereby reducing the influence of motion-contaminated observations while preserving geometrically valuable features around object boundaries and occlusion regions. Unlike conventional hard semantic masking strategies, Boundary-SGDP provides a soft and adaptive mechanism for handling dynamic observations. Experiments conducted on four dynamic walking sequences from the TUM RGB-D benchmark demonstrate that the proposed method achieves lower absolute and relative trajectory errors than the original ORB-SLAM3 system, while retaining substantially more boundary-related features. The results confirm the effectiveness of semantic–geometric fusion and boundary protection for robust visual localization and mapping in dynamic indoor scenes, and demonstrate the potential of the proposed framework for practical autonomous navigation and intelligent perception applications. Full article

The inspection of conventional door locks in public buildings is a repetitive security task commonly performed manually at predefined times. This paper presents the development and experimental validation of a non-invasive, low-complexity robotic system designed for autonomous door lock-state verification. The core contribution is a novel physical-interaction method integrated into an indoor omnidirectional mobile robot that infers the lock state of a lever-type door handle without infrastructure modifications. The system executes a three-stage operational workflow: 2D LiDAR-based global positioning in front of target doors, depth-camera-based local realignment of the robot and the door handle, and physical actuation coupled with state inference via kinematic feedback. By depressing the handle during a controlled forward motion, forward displacement identifies an unlocked door, whereas motion resistance signals a locked state. The system was evaluated in a real facility across 18 target doors during eight complete inspection missions. Out of 144 verification attempts, the system achieved a 97.9% success rate; LiDAR global positioning enabled immediate handle actuation in 96 cases (66.7%), while depth-camera realignment successfully corrected 45 handle misalignments. These results validate the reliability and low-complexity of physical-feedback inference for routine autonomous facility security. Full article

Accurate and smooth path tracking is important for autonomous tracked agricultural robots operating in greenhouse-like environments. Existing adaptive look-ahead pure-pursuit methods mainly adjust the look-ahead distance according to vehicle speed or path geometry, while the influence of time-varying localization reliability has not been sufficiently considered. This study proposes a noise-aware adaptive pure-pursuit controller that combines Extended Kalman Filter (EKF) estimation with causal Savitzky–Golay (SG) endpoint smoothing. A bounded look-ahead law is designed by jointly considering normalized vehicle speed, lateral error, path curvature, and an innovation-derived localization-noise indicator. Numerical simulations were conducted on straight, circular, S-shaped, and U-shaped reference paths under prescribed localization disturbances. Under the 0.5 m positional-noise condition, the proposed method achieved an root mean square error (RMSE) of 0.087 m and an angular-velocity root mean square (RMS) of 0.28 rad/s, compared with 0.112 m and 0.36 rad/s, respectively, for conventional fixed-look-ahead pure pursuit. Compared with proportional-integral-derivative (PID), Stanley, model predictive control (MPC), and conventional pure-pursuit controllers, the proposed method provides a favorable balance between tracking accuracy and control smoothness. It also has better computational efficiency than MPC while retaining the low-computational-burden advantage of geometric control. In the sensitivity analysis, the relative RMSE increase from 0.1 to 0.8 m was 36.5% for the proposed method and 103.4% for conventional pure pursuit. These results indicate that the proposed lightweight noise-aware control strategy can improve tracking accuracy, control smoothness, and tolerance to localization disturbances under the specified numerical conditions, providing a practical design reference for low-speed greenhouse agricultural robots. Full article

Occupant-centric building operation requires timely information on occupant states and local indoor conditions, but fixed sensors provide limited spatial coverage, and wearables depend on user participation. This study develops a Robot Operating System 2 (ROS 2)-based wheeled mobile sensing platform for buildings. The main contribution is the integration of autonomous mapping and navigation, target approach, multisensor occupant-data acquisition, lightweight edge-based human detection, and return-to-dock operation on a Raspberry Pi 5. During operation, the robot patrols indoor locations, detects and approaches occupants, collects human and environmental data, uploads the data to a server, and returns to the charging dock. To support concurrent perception and navigation, YOLOv5n was compressed using structured channel pruning and multi-scale feature distillation. The compressed model reduced parameters and computation by approximately 53% and 61%, increased inference throughput from 7.5 to 16.5 frames per second, and enabled the robot to complete all 60 controlled trials across five locations and three postures. This study does not quantify HVAC energy savings or carbon-emission reductions. Instead, it validates a mobile sensing and edge-perception layer for future server-side thermal comfort inference, demand-responsive HVAC control, and evaluation of building energy and carbon performance. Full article

University technology campuses contain specialized laboratories, academic programs, and services that can be difficult for first-time visitors to identify. This paper presents INDI, a custom mobile campus guide robot that combines spoken interaction, synthesized speech, touchscreen feedback, animated facial states, head motion, and predefined mobile guidance behaviors. The platform retains the modular mechanical concept of an earlier prototype while replacing its Raspberry Pi and open-loop remote-control architecture with an NVIDIA Jetson Nano, an Arduino Uno motor-control bridge, ROS 1 nodes, encoder feedback, and dual PID speed loops. The robot weighs 2.37 kg, measures $39\times 27\times 69.5$ cm, reaches a software-limited maximum speed of 0.4 m/s, and provides 27 min of continuous operation in the reported tests. Ten repetitions of each motion test produced mean displacements of 1.058 m and 2.182 m for 1 m and 2 m commands, respectively, and mean rotations of 89.2° and 180.3° for 90° and 180° commands. Voice trials achieved 90% correct interaction in a quiet environment and 70% under nearby conversational noise. In an exploratory user study with 13 participants, 16 of 20 assigned tasks were completed and the mean overall rating was 4.31/5. The results demonstrate the feasibility of an integrated, modular, physically embodied information service, while also revealing accumulated linear-motion error, sensitivity to ambient speech, limited battery duration, and the need for grounded institutional knowledge and autonomous localization. These findings are presented as preliminary evidence of technical and interaction feasibility rather than as confirmatory evidence of usability or campus-scale autonomous navigation. Full article

Image segmentation is a fundamental step in computer vision and a cornerstone of robotic perception, serving as the foundation for interpreting data acquired from vision sensors, enabling robots to analyze complex visual environments, identify and localize objects, and support intelligent decision-making and autonomous control. It plays a critical role in applications such as autonomous navigation, robotic manipulation, medical robotics, agricultural robotics, autonomous vehicles, and human–robot interaction. Image segmentation has evolved from classical methods, which relied on handcrafted rules and mathematical models, to deep learning approaches that learn complex visual patterns directly from data. This evolution reflects advances in algorithms, computational power, and the theoretical foundations of mathematics and data science. Modern deep learning methods rely heavily on large, well-annotated datasets to train sophisticated neural networks. Yet, classical techniques remain valuable in certain scenarios, offering faster, reliable results without extensive computational requirements. Understanding the strengths and limitations of both approaches is key to selecting the right method. This paper surveys image segmentation techniques, comparing them in terms of accuracy, computational cost, and processing speed to guide informed method selection. Full article

Conventional image acquisition requires an electrically powered image sensor to be placed directly behind the camera lens, constraining camera placement. To overcome this issue, we introduce M3-RGB as an incoherent-light fiber imaging system in which a multicore, multimode optical fiber passively relays lens images to a remotely located image sensor. Unlike conventional approaches, M3-RGB is designed to operate directly on incoherent light and requires no electrical power or active components at the sensing interface. Because propagation through the fiber yields spatially scrambled patterns, a neural network is used to reconstruct the original scene by exploiting the spatial locality preserved by the multicore structure. In a controlled optical bench setup, where a liquid crystal display monitor displays road-scene images, we construct a paired dataset of scrambled and ground-truth images and quantitatively evaluate reconstruction performance across different fiber core counts, fiber lengths, and calibration settings, utilizing the peak signal-to-noise ratio and structural similarity index measure as performance metrics. By decoupling imaging electronics from the sensing point, this passive remote image relay approach may expand sensor placement options for potential applications such as all-around perception for mobile robots and autonomous vehicles, surveillance, and inspection in confined spaces. Evaluations in real outdoor environments constitute future work. Full article

Tower cranes are indispensable to building construction, but routine lifting still depends on the coordinated judgement of crane operators and signalpersons, which constrains automation and introduces human-factor variability. This study develops and formalizes an AI agent-driven framework for Level-4 (L4) robotic tower cranes with human supervision. The framework development comprises formal definitions of the digital signalperson agent and digital crane operator agent, a two-phase task–commit protocol, a supervisory finite-state machine, and a maximum-risk decision structure for task acceptance, degraded operation, minimum-risk stopping, and human takeover. Experimental validation was intentionally limited to three operational interfaces: terminal localization, autonomous positioning, and takeover-command latency. Under the tested site conditions, the terminal-localization error had a mean of 3.94 cm and a 90th percentile of 7.11 cm; the autonomous-positioning error averaged 48.2 cm, with 90% of observations within 80 cm; and the takeover-command latency remained below 500 ms, with a maximum of 458 ms. These measurements support the engineering feasibility of the tested supervised-autonomy interfaces, but they do not constitute full validation of the complete L4 safety case, including dynamic risk estimation, obstacle avoidance, adverse-weather operation, blind lifting, or multi-crane coordination. Full article

This work presents the design, construction and operation of the SKD-1, a modular skid-steer unmanned ground vehicle (UGV) developed as a low-cost research platform for mobile robotics applications. The platform integrates a differential skid-steer drive system, a Raspberry Pi-based onboard computer, and a microcontroller-based control layer implemented using an STM32 microcontroller. The sensing system includes light detection and ranging (LiDAR), global navigation satellite system (GNSS), and an inertial measurement unit (IMU), enabling experiments in localization, mapping, and autonomous navigation. The software architecture is based on the Robot Operating System (ROS) 2 framework, relying on standard ROS 2 packages for perception, mapping, and path planning, with the custom hardware-interface layer being the only non-standard software component. The mechanical and electronic subsystems were designed with a modular architecture that facilitates maintenance, sensor replacement, and hardware upgrades. The primary contribution of this work is the open-hardware design, integration, and documentation of a reproducible robotics testbed, motivated by the prohibitive cost of commercial platforms in resource-constrained research contexts. Indoor and outdoor experiments—covering velocity-tracking, SLAM, and waypoint-navigation trials—demonstrate the functional integration of the sensing, actuation, and computing subsystems. Full article

We present a nature-inspired approach for self-organizing logistics in contested environments characterized by uncertainty. The presented approach leverages territorial partitioning principles observed in natural collectives and systems. The logistics challenge addressed involves autonomously deploying a fleet of uncrewed mobile robots tasked with dynamically adjusting their positions to optimally service spatially and temporally fluctuating demands. Unlike traditional logistics, our approach is largely decentralized, relying almost exclusively on local interactions and decision-making. Monte Carlo simulations are conducted to evaluate performance across different scenarios, varying in client distribution (from uniform to highly clustered) and the range of the local perception of the logistic platform. Results demonstrate that the decentralized allocation strategy can deliver logistics support at a performance on par with a traditional clustering method, such as k-means, at runtime and without preliminary offline calculations. Full article