Search Results (3,229)

Apple picking is an inherently labor-intensive, time-consuming, and costly task, and robotic harvesting represents a potential alternative to address this challenge. This study presents the development and field evaluation of an integrated robotic system for apple harvesting, which combines machine vision, a dual four-degree-of-freedom (DoF) manipulator, and a mobile platform. The harvesting mechanism employed a streamlined 4-DoF manipulator driven by closed-loop stepper motors, incorporating a differential gear mechanism to execute yaw and pitch motions. Trajectory planning utilized linear interpolation with a harmonic acceleration/deceleration profile to ensure smooth end-effector movement. Fruit detection and localization within the canopy were performed by a stereo vision system running a lightweight deep neural network, achieving a mean hand-eye calibration accuracy of 4.7 ± 2.7 mm. Three negative-pressure driven soft end-effector designs—a suction soft end-effector (SSE), a grasping soft end-effector (GSE), and a suction-grasping soft end-effector (SGSE)—were assessed for their harvesting performance. Field trials conducted in a commercial spindle orchard demonstrated that the GSE achieved the highest performance, with a harvesting success rate of 80.80% among reachable fruits, a full-process success rate (from detection to collection) of 61.59%, an overall fruit damage rate of 10.89%, and an average single-fruit cycle time of 5.27 s. In contrast, the SSE and SGSE showed lower success rates (49.21% and 64.71%, respectively). This work provides a practical robotic harvesting solution. It validates the feasibility of a zoned, multi-manipulator harvesting strategy and delivers comparative data to guide the development of more efficient and robust harvesting robots. Full article

The A* algorithm is widely used in path planning for Automated Guided Vehicles (AGVs), but the path it generates is prone to collision with random obstacles. To address this issue, this paper proposes a hybrid path planning algorithm integrating the improved A* algorithm with Dynamic Window Approach (DWA). Firstly, a global key point extraction strategy is adopted, and Bresenham’s line algorithm is used to eliminate redundant path points and turning inflection points, optimizing the conciseness and continuity of the path while redefining the child nodes of the current position. Secondly, in complex environments, the inflection points of the global path are taken as the target points of DWA to segment the path, and local dynamic planning is combined to achieve real-time obstacle avoidance. Simulation results show that compared with the traditional A* algorithm, the improved algorithm reduces the planning time by 24.19%, decreases the number of inflection points by 40.00%, and shortens the path length by 1.49%. In environments with random obstacles, the path generated by the hybrid algorithm is smoother, which can effectively enhance the local obstacle avoidance capability and improve the safety of path planning. Furthermore, physical experiments on an AGV platform with a distributed master-slave control architecture (STM32 microcontroller and Jetson embedded processor) verify the algorithm’s hardware compatibility and real-time computing performance, validating its engineering applicability in practical industrial scenarios. Full article

Visual localization plays a critical role for mobile robots to estimate their position and orientation in GPS-denied environments. However, its efficiency, robustness, and generalization are fundamentally undermined by severe viewpoint changes and dramatic appearance variations, which present persistent challenges for image-based feature representation and pose estimation under real-world conditions. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm for lightweight deployment and privacy isolation on edge devices, while how to learn compact and invariant image tokens without relying on structural 3D maps still remains a core problem, particularly in highly dynamic or long-term scenarios. In this paper, we propose the Debiased Multiplex Tokenizer as a novel method (termed as DMT-Loc) for efficient and versatile MFVR to address these issues. Specifically, DMT-Loc is built upon a pretrained vision Mamba encoder and integrates three key modules for relative pose regression: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions. Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and autoregressive pointer attribution. Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a symmetric swap gating mechanism operating inside each decoupled regression head to support accurate and flexible pose prediction in both pair-wise and multi-view modes. Extensive evaluations across seven public datasets demonstrate that DMT-Loc substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments. Full article

Soft actuators provide a wide range of motion capabilities, allowing for the advancement of novel mobile robots. However, soft actuators that possess the capability required to achieve three-dimensional movement are limited. In addition, the presence of air supply tubes poses a challenge to utilizing pneumatic actuators as mobile robot components. This study presents a long inflatable actuator with a novel structure in which air supply tubes are arranged within its body. This structure enables the extension of the inflatable tube with minimal deformation. The proposed actuator comprises an inflatable tube and a spool mechanism. The length of the actuator is controlled by a motor. The performance of the actuator was evaluated experimentally, validating its alignment with our proposed models. The results showed that the proposed actuator exerted extension and contraction forces of 28 N and 87 N, respectively. Furthermore, the proposed actuator can be equipped with a gripper at its tip, enhancing its functionality. In a demonstration, this gripper-equipped actuator successfully extended to grasp a bar at a height of 1.3 m and contracted while lifting a 1.0 kg base. This demonstration indicated that the proposed actuator could provide the required arm motions of a bi-arm climbing robot. Full article

Autonomous mobile robots are transforming industries from e-commerce logistics to field exploration, but their effectiveness depends on onboard energy storage. This study addresses the challenge of selecting optimal battery technologies for autonomous mobile robots, balancing performance, energy efficiency, thermal stability, and cost across diverse applications. We focus on lithium-ion, lithium-polymer, and nickel-metal hydride batteries, the most common power solutions, each with distinct advantages and disadvantages in energy density, form factor, thermal stability, and cost. A dynamic modeling and simulation framework in MapleSim evaluated these chemistries under defined and representative operating conditions, tracking state of charge and temperature during charging and discharging. A Particle Swarm Optimization algorithm evaluated 37 battery configurations by thermal stability, energy efficiency, and cost across five use cases. Key results indicate that for logistics and warehousing, lithium nickel manganese cobalt oxide with graphite is optimal; for healthcare, lithium nickel manganese cobalt oxide with lithium titanate oxide excels; for manufacturing, lithium nickel cobalt aluminum oxide with graphite leads; for agricultural robots, lithium manganese oxide with graphite is best; and for exploration and mining, lithium iron phosphate with graphite is most reliable. These results provide a structured basis for battery selection, showing how simulation-driven, multi-criteria decision-making enhances energy management and operational reliability. Full article

Surgical navigation and augmented reality (AR) are widely used in neurosurgery, spinal surgery, and orthopedics. However, their use in minimally invasive abdominal and thoracic soft-tissue surgery is limited, as tracking deformable, mobile organs is challenging. Recent advances in AR may address these challenges to improve intraoperative navigation. This systematic review, registered in PROSPERO (2024) and based on PRISMA guidelines, analyzes literature from 2014 to 2024 about AR in minimally invasive abdominal and thoracic soft-tissue surgery. It identifies target organs, describes AR hardware and software, and evaluates accuracy levels, usability outcomes, clinical benefits, technical limitations, and research needs. Searches of PubMed, Web of Science, and Embase for English-language studies found 1297 records, of which only 28 (2%) met the inclusion criteria. Nearly half (n =12; 42%) focused on liver surgery; none on gynecologic surgery. The AR devices varied in tracking methods, image processing, visualization, and display. Overall, AR improved anatomical guidance and procedural planning, especially in complex surgeries. Integration with robotic systems may further boost visualization, precision, and workflow, though challenges remain in standardization, large-cohort validation, and workflow integration. Full article

Corrosion in steel pipelines can cause critical failures in industrial systems, while conventional inspection methods such as radiography and ultrasonic testing are costly and require specialized personnel. This study presents a mobile computer vision system for automated corrosion detection inside steel pipes using deep learning-based visual analysis. The proposed system consists of a Raspberry Pi 4-based mobile robot equipped with a high-resolution camera for internal inspection. Acquired images were processed using color-space transformations (RGB–HSV), filtering, and segmentation. Convolutional neural networks and semantic segmentation models, including YOLOv8-seg (Instance segmentation) and DeepLabV3 (Semantic segmentation), were trained on a custom corrosion image dataset to identify corroded regions. Real-time visualization was implemented via Flask-based video streaming. Experimental results demonstrated high detection accuracy for uniform corrosion, achieving a mean Intersection over Union (mIoU) above 0.98 and a precision of 0.99 with the YOLOv8-seg model. These results indicate that the proposed system enables reliable and automated corrosion inspection, with the potential to reduce inspection costs and improve operational efficiency. Future work will focus on enhancing real-time performance through hardware optimization. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p < 0.01) across all evaluated metrics. Full article

Flexible and safe object handling in modern industrial environments increasingly relies on mobile robotic systems capable of both dexterous manipulation and adaptive motion. However, when wheeled mobile manipulators (WMMs) operate under heavy or dynamically varying loads, challenges arise in maintaining sufficient force exertion capability and achieving stable coordination, particularly during cooperative transportation. In this paper, we present a unified framework to address these challenges with three main contributions. A quadratic-programming-based redundancy resolution scheme incorporating a load-capacity maximization metric is developed to explicitly enhance the force exertion capability of the system under heavy loads. A variable-admittance cooperative control strategy for dual-WMM transport is proposed to ensure synchronized motion and adaptive force regulation during collaborative manipulation. In addition, a unified framework that integrates configuration optimization with compliant cooperative control is established, enabling strict constraint enforcement, improved load capacity, and robust coordination between the two WMMs. Extensive simulations demonstrate the effectiveness of the proposed methods in improving load-handling performance and ensuring coordinated, compliant cooperative manipulation. Full article

Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly in the presence of sustained external disturbances and modeling errors. This paper presents a systematic modeling and control study using a three-degrees-of-freedom sagittal plane representation derived from the original six-degrees-of-freedom dynamics. Two linear tracking controllers are designed and compared: a full state feedback tracking controller and a linear quadratic servo controller with integral action. Practical performance is validated through real-time hardware in the loop simulation, where the controller runs on embedded hardware and the plant is executed on a real-time target including discrete time-sampling effects and analog input output communication noise associated with signal transmission. The results show that both controllers achieve stabilization, while the comparative HILS results reveal a trade-off rather than a uniformly superior controller. The full state feedback controller often yields lower finite-horizon position tracking errors, whereas the linear quadratic servo controller provides tighter body-pitch regulation and the more reliable removal of steady-state offset under sustained constant disturbances. These results demonstrate the feasibility of optimal servo control on cost-effective embedded platforms and indicate that controller selection should depend on the desired balance, considering tracking accuracy, disturbance rejection, convergence behavior, and actuator usage. Full article

Mobile robot path planning using Ant Colony Optimization (ACO) has the disadvantages of slow convergence, local optima, and unsmooth paths because of fixed heuristics and constant pheromone updating. In this paper, Self-Adaptive Risk-Aware Bidirectional updating ACO (SAR-BACO) is proposed with three improvements: (1) composite heuristic incorporating target attraction, obstacle repulsion and direction consistency to minimize early blind searching; (2) dynamic pheromone updating based on solution quality and number of iterations to balance exploration and exploitation; (3) triangular pruning to remove redundant turning points and become smoother. Theoretical analysis verifies convergence. Our experimental results on grids up to 50 × 50 demonstrate that SAR-BACO performs much better than classical and heuristic-improved algorithms with respect to the length of a path, convergence rate, smoothness and efficiency. Using SAR-BACO on a 50 × 50 map, the path lengths, convergence iterations and turning points decreased by 60.68%, 48.96%, and 96.00% respectively compared to Basic ACO (after triangular pruning, values averaged over 50 runs). The framework provides a generalizable solution to autonomous navigation with the need to consider both search efficiency and path executability. Full article

This paper highlights a communal fault detection and isolation framework integrating a convolutional neural network (CNN) with a finite-state machine (FSM). The proposed concepts ensure state-based controlled discriminate pattern recognition and enable the diagnosis of different anomalies in the mobile robot in a multi-robot environment. The framework processes the time-series sensor data through the convolution layer upon experiencing different types of fault and governs different states based on fault diagnosis and recovery. The proposed concept has been validated using a Python 3.11 and Webot environment featuring the shrimp robot in a multi-robot arena. The model obtained an accuracy of 97% in identifying and classifying faults, enabling automated recovery of faulty robots in the multi-robot environment. Experiments conducted on different simulators demonstrate that effective fault management can be achieved with low training loss. Full article

To address the limitation of wheeled mobile robots in traversing unstructured terrain, this paper proposes an Active–Passive Hybrid Obstacle-Crossing Wheel (APHOCW). The mechanism integrates an active angle-adjustment mechanism and a lever-assist mechanism. While maintaining low system complexity and high reliability, it utilizes periodically telescoping assist levers that rotate with the wheel to overcome obstacles. By actively adjusting the eccentric angle, the trajectory of the assist levers can be modified to optimize the crossing posture. Through geometric and quasi-static mechanical modeling, dynamic simulation, and prototype experiments, this study systematically validated the robot’s obstacle-crossing capability and continuous step-climbing performance under different eccentric angles. Simulation and experimental results demonstrate that in the lever-assisted obstacle-crossing mode, the robot can stably overcome obstacles with a height up to 2.1 times its wheel radius and accomplish continuous step ascent. A smaller eccentric angle helps increase the maximum obstacle-crossing height, while a larger eccentric angle exhibits superior energy efficiency under sufficient ground-friction conditions. Full article

This study focuses on the challenges and opportunities of integrating industrial robots into robotic bricklaying systems (RBSs) for automation and digital transformation in the construction industry. A mobile RBS was designed, engineered, manufactured and commercially implemented for the first time in Poland. The RBS is designed to perform robotic bricklaying in situ in municipal, residential, and industrial buildings, where sustainable construction tasks are implemented. The details of the design solutions for the RBS, virtual simulation, and real robotic bricklaying processes are presented. The results of bricklaying using the RBS and the factors that influence the robotic bricklaying process are summarized. A 3D digital building information model (BIM) created using Autodesk Revit tools was used for simulated robotic bricklaying in the ABB RobotStudio 2025.5 program, from which they were transferred to the programming of the ABB IRB 4600 bricklaying robot. The laser programming method for the bricklaying robot, bricklaying procedures, and algorithms are also presented. The costs of human labor and robot construction were compared, and the return on investment (ROI) was calculated. RBS evaluations were performed in laboratory settings, on-site demonstrations, and commercial wall-laying in residential apartments. Full article