Visual fiducial markers, such as ArUco, are widely used in robotics for camera calibration, pose estimation, and autonomous navigation. However, conventional single-scale markers inherently suffer from a trade-off between long-range detectability and close-range accuracy, as marker size cannot be optimized for both simultaneously. To address this challenge, recent works have explored multi-scale hierarchical markers that embed smaller tags within larger ones, enabling coarse detection at a distance and precise localization at close range. In this work, we further advance this concept by presenting a scalable and fully configurable implementation of embedded ArUco markers (eArUco). The original two-level eArUco approach is enhanced through multi-level hierarchical nesting, major-color-based selection of an inner marker, and the introduction of an additional white boundary for inner markers. We present HeArUcoInterface, a software suite for the automatic generation of hierarchical eArUco (HeArUco) markers, supporting both command-line interface and graphical user interface modes. The tool outputs printable marker textures and SDF models compatible with the Gazebo simulator, facilitating straightforward integration into both simulated and real robotic systems applications. Virtual experiments of the Servosila Engineer tracked mobile robot positioning, using HeArUco markers, demonstrated improved detection stability, a wider operational localization range, and more accurate pose estimation compared to traditional single-scale markers.

This article considers a dual problem of optimizing field coverage while minimizing soil compaction and managing the energy constraints of agricultural robots. The soil compaction in precision agriculture is a major challenge, as mobile robots are becoming increasingly common in field operations. A proposed optimization combines a soil compaction risk assessment with energy-efficient trajectory planning for a fleet of mobile agricultural robots. The algorithm uses a grid representation of a field, where each cell is assigned a compaction risk value using a function, which allows clustering cells into zones with similar characteristics of the soil compaction risk. Within these zones, maximum permissible velocities of agricultural robots are determined. The Boustrophedon algorithm generates optimal coverage paths for each zone to minimize turns and ensure complete coverage. A fitness function balances multiple objectives, including soil impact, path length, and energy constraints. To eliminate energy constraints, a genetic algorithm is used that simultaneously optimizes the placement of static charging stations and the distribution of cover paths among a tractors’ fleet. The system balances soil conservation and requirements by adapting a robot's velocity to each zone. The computational experiments for various types and sizes of agricultural fields demonstrated the effectiveness of the proposed approach.

Due to compliance and significant friction in the drive mechanism, tendon-based minimally invasive surgical robot systems exhibit highly nonlinear behavior and low position control accuracy. In this study, a novel power transmission mechanism incorporating both tendon position and tension feedback was developed. The mechanism is controlled by a corresponding collocated controller based on position sensing and a cable tension sensing method, which enables compensation for nonlinearities in the motion transmission of the tendon-based surgical robot system. The collocated controller was implemented and experimentally validated on a surgical instrument utilizing the power transmission mechanism. Experimental results demonstrate that position tracking in the motion transmission of the tendon-driven instrument can be improved by 80% using the proposed mechanism and controller. This advancement in robotic surgery holds significant potential to enhance the precision and effectiveness of surgical procedures.

The agricultural sector is undergoing a digital transformation due to modern automation, robotics, sensing, and simulation technologies. This research explores the use of digital human models (DHMs) in the Gazebo virtual environment to enhance agricultural workflows, improve human–robot interaction, and ensure safety. We propose a framework that models typical agricultural scenarios, such as field mapping, harvesting efficiency control, crop inspection, obstacle avoidance, and theft detection. DHMs represent farm workers interacting with mobile autonomous systems, stationary sensors and sensor networks. The DHMs are equipped with generic and task-specific animations; the latter include such activities as crop harvesting and field inspection. The simulation environment features agricultural settings with dynamic obstacles and predefined work zones. Performance in each scenario is proposed to be evaluated using metrics such as a task’s completion time and obstacle avoidance rate. Results of preliminary simulation of the proposed simplified scenarios in the Gazebo simulator demonstrated a high potential of DHMs and Gazebo to optimize agricultural workflows and improve human–robot interaction. This study provides a foundation for leveraging simulation technologies to address practical challenges in agriculture and to support the design and validation of intelligent agricultural systems.

This paper investigates the application of monocular depth estimation for 3D object localization in collaborative robotic manipulation tasks. We propose a method for converting relative depth maps obtained using the Depth Anything V2 Small model into metric depth maps by employing small 5 × 5 cell ArUco markers with a physical size of 3 × 3 cm, which do not obstruct the operator or reduce the available workspace. Pre-calibration on 10–30 images achieves a diameter measurement accuracy with a mean absolute error (MAE) of 0.307–0.671 cm (relative error 3.6–7.9

Mobile robots are increasingly used to automate repetitive and labor-intensive tasks in environments such as hospitals, warehouses, offices, and manufacturing facilities. When working together, multiple robots can achieve greater efficiency by sharing workloads and coordinating complex tasks, which enables faster completion of large-scale operations. Managing such teams of robots, however, requires an intuitive system for their control. This research presents a high-level graphical user interface (GUI) for controlling a group of mobile robots. The GUI simplifies robot management by providing a user-friendly interface for task assignment, monitoring and continuous control in semi-autonomous and teleoperational modes. It establishes a seamless ROS-based communication between robot hardware and software layers. The developed software was validated in the Gazebo simulator and real environment using the ArtBul mobile robot. Results demonstrated that the GUI successfully controls mobile robots within hospital and office environments, and could be further extended for multiple agents, including robots of different types, sensors and IoT-based infrastructure.

Robotic injection devices represent a significant advancement in medical technology, aimed at improving efficiency and accuracy of drug delivery. By utilizing advanced mechanisms, healthcare professionals can administer treatments with greater ease, thereby enhancing patient comfort and compliance during medical procedures. In this paper, an injection device attached to a robotic arm has been designed for administering medications into a human body. It comprises a syringe attached to a cylinder and a piston mechanism operated using a pneumatic power source. Design of the injection device is generated using a CAD software. Motion capabilities of the device are validated using a simulation study.

Prosthetic hands are vital assistive devices that significantly enhance quality of life for individuals with upper limb amputations, enabling them to regain autonomy and perform essential daily activities. However, many existing prosthetic solutions are hindered by high costs, excessive weight, and complex actuation systems that limit accessibility and usability. This paper introduces a novel prosthetic hand design that addresses these limitations through a mechanically efficient and cost-effective approach. The proposed system employs five motors, one per finger, combined with a closed-loop chain linkage mechanism that transmits motion across joints of each finger. This eliminates a need for multiple actuators per finger, thereby reducing an overall weight, power consumption, and cost of the device. We present a complete design methodology, a mechanical architecture, and functional analysis of the prototype. The mechanical structure achieves finger flexion up to 125 degrees, distal interphalangeal (DIP) joint articulation up to 90 degrees, and wrist deviation of ±20 degrees, closely mimicking natural hand movement. The results demonstrate feasibility and advantages of the approach, offering a promising direction for developing affordable, efficient, and user-friendly prosthetic hands.

Autonomous agricultural vehicles operating under the Controlled Traffic Farming (CTF) paradigm face complex routing challenges when minimizing soil compaction, total mission time, and station placement under battery constraints. This paper introduces Multi-Objective Coordinated Autonomous Routing and Placement with Fixed Lanes (MO-CARP-FL), a novel multi-objective evolutionary algorithm designed to optimize the coordinated routing of homogeneous autonomous tractors over a predefined field traffic lane. The algorithm simultaneously addresses five conflicting objectives: minimizing soil compaction using a logarithmic saturation model, minimizing total route time, reducing the number of charging stations, preserving spatial coherence in assigned routes, and balancing workload among tractors. Chromosomes encode both routing and station placement decisions, and custom crossover and mutation operators preserve structural feasibility. A soil compaction model and energy-aware constraints are integrated into the evaluation function. Experimental simulations demonstrate that MO-CARP-FL produces environmentally sensitive routing plans while reducing field degradation. The proposed method is validated through CTF field scenarios, and its results are visualized to provide interpretable insights into route distribution, station usage, and soil impact. This work contributes to multi-objective optimization in agricultural logistics by addressing both environmental impact and operational efficiency in autonomous field operations.

The Southeast Asia region is vulnerable to extreme precipitation, leading to hydrological disasters that endanger lives and infrastructure. Rapid response measures necessitate search and rescue operations, where rescue robotics can replace human rescuers and provide supplementary capabilities. Effective rescue efforts require information on victim whereabouts and area mapping, necessitating the development of AI-based information systems. This paper outlines an international framework for using heterogeneous robotic teams and developing information collection systems for hazardous site rescue management. The approach leverages expertise in urban search and rescue robotics from Japan, Thailand, and Russia, countries frequently affected by high precipitation and climate change. The joint research aims to create a new framework and control strategies for cooperative behavior among international robotic teams, focusing on interaction protocols, mapping agreements, data fusion, and other collaborative features. The robotic teams comprise various unmanned ground vehicles, aerial vehicles, underwater vehicles, and surface vehicles. These teams provide local data through sensing and mapping activities from water surfaces, underwater, air, and terrain to create a comprehensive disaster site map. The collaborative framework relies on path planning, disaster area coverage algorithms, control strategies, and multi-robot joint SLAM technologies for heterogeneous teams. Robot Operating System (ROS) and Gazebo simulator are used for modeling and validating the algorithms.

Unmanned aerial vehicles (UAVs) are increasingly used in applications such as inspection and search and rescue, yet safe and intuitive teleoperation remains challenging in cluttered and GPS-denied environments. This paper presents a modular haptic teleoperation framework that integrates Unity, ROS2, optical motion capture, a physical UAV platform, and a stylus-based haptic device for bidirectional human–robot interaction. Operator inputs are mapped to UAV velocity commands, while obstacle proximity is rendered as continuous haptic feedback to enhance spatial awareness. A Control Barrier Function (CBF)-based command filtering layer is incorporated to modify unsafe velocity commands in real time. The system is evaluated through both Unity-based simulation and real-world experiments using a DJI Tello UAV and OptiTrack motion capture. In the virtual experiments, four conditions were compared: baseline, CBF only, haptic only, and CBF + haptic. The combined CBF + haptic condition reduced the average task completion time from 62.9 s to 42.8 s and resulted in no observed collisions under the evaluated virtual scenarios. The real-world experiments further confirmed stable force–distance behavior, bounded latency, and feasible haptic-assisted UAV navigation in a constrained indoor environment. These results indicate that combining haptic feedback with CBF-based safety control can improve teleoperation efficiency, safety, and usability under the tested conditions while providing a practical step toward simulation-to-real haptic UAV teleoperation.

This paper presents a monocular vision-based relative localization system for a collaborative robotic cell comprising a mobile conveyor and an industrial robot. The main challenge is that movement of the conveyor and/or the robot may disrupt traditional camera-robot calibration, which requires time-consuming manual readjustment. The proposed solution enables a camera, statically mounted on the conveyor frame, to detect a calibration ArUco marker attached to the robot base with known coordinates, compute the camera’s position relative to the robot, and then determine the coordinates of a target object on the conveyor within the robot base coordinate system. The key advantage of this approach is that calibration is performed online in each frame, allowing the conveyor and the robot to be repositioned independently between production cycles without requiring manual recalibration. A mathematical model of coordinate transformation, including rotation matrices and homogeneous transformations, is developed. The system is integrated with a real KUKA KR3 robot. The experiments demonstrated a mean absolute error in the XY plane of 2.4 mm for the X-axis and 3.1 mm for the Y-axis, which is acceptable for most object grasping tasks. These findings confirm the system’s functionality and suitability for flexible manufacturing cells with frequently changing layouts.

The paper considers the problem of implementing a simultaneous localization and mapping (SLAM) system for mobile robots with limited computing resources in an agro-industrial complex. The high cost of industrial navigation solutions makes them economically impractical for automating monitoring tasks in greenhouses, warehouses, and other agricultural facilities characterized by extended geometry and limited lighting. An architecture based on a Raspberry Pi 4 single-board computer, an ESP32 microcontroller and an LDROBOT LiDAR integrated into the ROS 2 ecosystem is proposed. A comparison of the SLAM Toolbox and Cartographer algorithms was performed, taking into account the computational and thermal limitations of the platform. Experiments in conditions simulating the configuration of extended indoor spaces have shown that the SLAM Toolbox ensures the metric consistency of the map, while Cartographer demonstrates systematic small-scale drift. The resulting system operates in real time with CPU utilization reaching up to 23

This study introduces an innovative control approach for deploying multiple unmanned aerial vehicles (UAVs) to monitor an unknown flood region. The proposed strategy is designed to optimally distribute UAVs across the flood-affected area while cooperatively estimating the extent of inundation. To achieve this, an adaptive coverage controller is developed based on Centroidal Voronoi Tessellation (CVT), incorporating a novel mechanism for dynamically updating the density function. Within this framework, the density function serves as an evolving representation of the estimated inundation areas, allowing UAVs to adjust their positions adaptively in response to real-time environmental changes. The effectiveness of the proposed control strategy is validated through simulations conducted in the ROS/Gazebo environment, demonstrating its capability to enhance the accuracy of flood monitoring and improve the spatial distribution of UAVs.

This paper presents a low-cost monocular computer vision system, specifically designed for robotic grasping with a KUKA KR-3 manipulator, for automated detection and 3D localization of circular objects (dairy bottle caps) on a conveyor belt. The system achieves acceptable accuracy with minimal hardware, utilizing only a single RGB camera and static ArUco calibration markers. The approach comprises two main modules: object detection based on the Hough Gradient Method for precise 2D localization, and monocular metric depth estimation using the Depth Anything V2 foundation model to generate a relative depth map, followed by scaling using ArUco markers. Experimental evaluation on a preliminary dataset of 45 images yielded a mean absolute error in depth estimation of 1.81 cm. This accuracy is considered sufficient for reliable grasping, as the error is compensated by gripping the object at its central height. The proposed solution offers a cost-effective alternative to expensive stereo or LiDAR-based systems, making it well-suited for flexible production lines in the dairy industry. The results clearly confirm the practical applicability of the monocular approach for Industry 4.0 automation in agro-industrial enterprises.

This paper proposes an obstacle avoidance strategy for an underwater robot that integrates Hidden Markov Chains (HMC) with a fuzzy controller. The HMC predicts a vector of future states, leveraging the maximum state probabilities to guide the behavior of the fuzzy controller. The proposed fuzzy controller utilizes five inputs: the robot’s state predictions (crisp sets), instantaneous linear velocity, underwater depth, and yaw and pitch velocities, modeled with Gaussian and sigmoid membership functions. It generates three outputs, also using Gaussian and sigmoid functions, corresponding to the robot’s actuators: the propulsion motor, the steering motor, and the ballasting motor, which operates through an integrated hydraulic piston system. The paper also develops physics-based models for the propulsion, steering, and ballasting systems, alongside sensor fusion models that provide real-time control feedback. Additionally, it presents the robot’s platform and system architecture, designed with multi-threaded, real-time control capabilities. Experimental and simulation results validate the effectiveness of the proposed strategy, demonstrating robust obstacle avoidance in underwater navigation.

This work addresses the problem of energy-constrained coverage of agricultural fields using a fleet of identical unmanned aerial vehicles (UAVs). Each UAV must complete a spraying mission over a designated area, subject to limited battery capacity. To ensure energy feasibility, static charging stations are deployed along the field’s perimeter. The agricultural field is first partitioned into subregions using Lloyd’s clustering algorithm, producing a balanced decomposition adapted to the number of UAVs. Within each subregion, an efficient coverage path is generated using the Boustrophedon method, with flight lines aligned with the longest region axis to minimize energy-intensive turns. The primary objective is to minimize the total mission completion time while accounting for recharging needs and stochastic wind conditions. Wind is modeled as a vector with random speed and direction, affecting UAV energy consumption depending on relative flight orientation. To assess solution robustness under uncertainty, the expected mission duration is estimated using a Monte Carlo simulation. Charging station locations are optimized using the Particle Swarm Optimization algorithm. Finally, a post-processing step iteratively reduces the number of stations to achieve more cost-effective configurations without a significant loss in performance.

A significance of quadruped robots lies in their unique design and functionality, which allow them navigating a variety of terrains with remarkable agility and stability. Unlike popular wheeled robots, quadrupeds mimic locomotion of four-legged animals, enabling to traverse uneven surfaces, climb obstacles, and maintain balance in challenging environments. This paper introduces a development of a new leg-wheel hybrid quadruped robot. А peculiar design of the quadruped robot allows the robot to function as a bipedal robot while performing stationary tasks that do not require to change its location in space; at the same time, wheels at legs’ endpoints allow fast locomotion on a flat rigid terrain. The dual functionality enhances its versatility and broadens a range of tasks it can perform, making it suitable for various applications in research and practical applications. Design procedures, modelling methodologies, and static structural analyses performed to finalize a structure of the robot are demonstrated in the paper.

Swarm robotics is a complex domain within multi-robot systems that encompasses formation control, movement control, and inter-UAV communication. Coordinates task execution requires effective swarm control, which relies on robust motion control algorithms and reliable data exchange mechanisms. In this work, we propose an operator-leader-followers approach for managing UAV swarms. It comprises the following components: centralized swarm control utilizing the Fixed Global Difference (FGD) algorithm for maintaining inter-UAV distances and facilitating communication; a follow-mode mechanism enabling followers to accurately track leader’s motion; and a point-by-point swarm flight path control system enhancing overall control accuracy. Implemented in the Robot Operating System (ROS) and validated in the Gazebo simulator, the algorithm’s performance is evaluated via the following simulation scenarios: -shape, parabolic, and circular flight paths. The paper discusses experimental results in virtual environments of the Gazebo simulator and demonstrates achieved precision in terms of absolute error and root mean square error (RMSE).

This research presents a comprehensive study on the design and implementation of a robust trajectory tracking system for autonomous agricultural robots. It introduces a unified kinematic model that integrates different rolling structures, facilitating performance across various robotic designs. The novel path planning method utilizes double spiraliform tracks to enhance movement efficiency in complex agricultural settings and generate flexible fields in terms of scale, orientation, and location, providing reference trajectory models. A sliding mode controller is developed to manage nonlinear dynamics and discontinuous input references, ensuring stability and precision during operation. The sliding approach was compared against four controllers: a linear feedback controller, a state-space feedback controller, a proportional controller, and a proportional-integral controller. The evaluation of accuracy and precision with respect to the input reference model showed similar performances across the controllers. However, the sliding approach proved superior when inputting nonlinear reference and discontinuous external perturbations, producing chattering metric errors averaging 0.94 m and mean = 0.012 m, for xy components, respectively A Lyapunov analysis confirmed the sliding mode controller stability during path tracking nonlinear dynamics, handling unpredictable operational conditions. Numerical simulations validated the controller's effectiveness, showcasing its robustness against external disturbances and its ability to maintain stability and precision during operation.