Search Results (64)

This study addresses an off-site construction (OSC) task: installing heavy prefabricated equipment modules at elevated positions inside existing structures. The task simultaneously demands multi-ton payload capacity, a lift height approaching 10 m, and holonomic maneuvering in narrow aisles; to the authors’ knowledge, no single reported platform satisfies all three. We present a heavy-duty, high-lift mobile robot that lifts 6 t to 8 m. Two active swerve-drive modules and three passive casters form a five-point asymmetric layout combining holonomic mobility with load distribution, and the lift unit functionally decouples the vertical stroke (four helical band actuators) from the lateral stiffness (four scissor-stabilizing mechanisms). Planar motion is partitioned into three driving modes with closed-form forward and inverse kinematics, and zero-velocity transitions remove the kinematic model mismatch and the instantaneous-center-of-rotation discontinuity of a single unified model. Prototype measurements confirmed the motor-sizing torque assumptions, and chassis finite element analysis showed a factor of safety above 2.0 under maximum payload and quantified the in-plane stress induced by kinematic mismatch. In two field deployments, the robot reduced personnel by 25.0–27.3%, equipment by 42.9–60.0%, and installation duration by 50.0–85.7% relative to the incumbent methods, thereby extending mobile robots from horizontal transport to vertical OSC module installation. Full article

Autonomous omnidirectional vehicles that combine redundant holonomic kinematics, ROS 2/micro-ROS implementation, and simulation-to-real validation remain limited in the literature. This paper presents an eight-wheel Mecanum autonomous mobile robot for campus navigation in environments shared with pedestrians. The work formulates forward and inverse kinematics for the redundant eight-wheel topology and implements a distributed architecture in which ROS 2 handles high-level navigation and micro-ROS connects ESP32-based wheel interfaces. The platform integrates LiDAR, stereo vision, inertial, encoder, and ultrasonic sensing within a closed-loop navigation stack. Validation was conducted through Gazebo simulation and physical experiments using an out-and-back navigation protocol. In the physical platform, 91 of 100 missions were completed without safety interruptions, with pose-accuracy success rates of 96% for outbound legs and 81% for return legs under $e_p<1.5\mathrm{m}$ and $|e_{\theta }|<{15}^{\circ }$. Median errors at the intermediate waypoint were $0.64\mathrm{m}$, $0.191\mathrm{m}$, and ${17}^{\circ }$, while final-pose medians after return were $1.016\mathrm{m}$, $0.573\mathrm{m}$, and ${28.5}^{\circ }$. These results provide a quantitative baseline for campus-scale redundant Mecanum navigation and identify heading recovery as the main limitation. Full article

The post-pandemic evolution of education involving mechatronics and machine learning has shifted the demand for robotic hardware from centralized laboratories to accessible laboratories in home environments. This paper presents a portable three-wheeled holonomic robotic platform designed for remote research and home office experimentation. The proposed system utilizes a modular design and low-cost philosophy comprising a custom embedded control system driven by an ESP32-WROOM microcontroller, which manages a closed-loop PID velocity controller using Hall effect feedback from three DC micromotors. In contrast, external nodes allow the reception, conditioning, and classification of 8-channel surface electromyography (sEMG) data sampled at 500 Hz. To address the non-stationarity and stochastic noise in raw sEMG signals, this study implements a hybrid Deep Learning (DL) architecture that complements 2D Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal context awareness. This model decodes the neuromuscular intent of the user into real-time holonomic velocity vectors, achieving validation accuracies of 80.51% for horizontal movement, 84.86% for vertical translation, and 99.56% for the Fist/no-Fist state. By synthesizing advanced AI-based teleoperation with a portable design, this study establishes a scalable framework for the next generation of “laboratory-at-home” educational tools and research regardless of physical location. Full article

Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models. Full article

The increasing heterogeneity of wheeled mobile robot (WMR) architectures, including differential-drive, Ackermann, omnidirectional, and reconfigurable platforms, poses a major challenge for defining a unified, scalable kinematic representation. Most existing formulations are tailored to specific mechanical layouts, limiting analytical coherence, cross-platform interoperability, and the systematic reuse of modeling, odometry, and motion-related algorithms. This work introduces a generalized kinematic modeling framework that provides a mathematically consistent formulation applicable to arbitrary WMR configurations. Wheel–ground velocity relationships and non-holonomic constraints are expressed through a concise vector formulation that maps wheel motions to chassis velocities, ensuring consistency with established models while remaining independent of the underlying mechanical structure. A parameterized wheel descriptor encodes all relevant geometric and kinematic properties, enabling the modular assembly of complete robot models by aggregating wheel-level relations. The framework is evaluated through numerical simulations on four structurally distinct platforms: differential-drive, Ackermann, three-wheel omnidirectional (3, 0), and 4WD. Results show that the proposed formulation accurately reproduces the expected kinematic behavior across these fundamentally different architectures and provides a coherent and consistent representation of their motion. The unified representation further provides a common kinematic backbone that is directly compatible with odometry, motion-control, and simulation pipelines, facilitating the systematic retargeting of algorithms across heterogeneous robot platforms without architecture-specific reformulation. Additional simulation studies under realistic physics-based conditions show that the proposed formulation preserves coherent kinematic behavior during complex trajectory execution and supports the explicit incorporation of geometric imperfections, such as wheel mounting misalignments, when such parameters are available. By consolidating traditionally separate derivations into a single coherent formulation, this work establishes a rigorous, scalable, and architecture-agnostic foundation for unified kinematic modeling of wheeled mobile robots, with particular relevance for modular, reconfigurable, and cross-architecture robotic systems. Full article

The use of simulated environments for the development and validation of Automated Guided Vehicles (AGVs) has proven to be an effective approach for reducing costs and accelerating the testing process. Simulated environments offer a safe and controlled means for performance analysis and controller parameter adjustment. However, most simulators employed for AGVs and mobile robots rely on kinematic models, which limits the fidelity of the tests. This work introduces a physics-driven Unity framework that leverages the NVIDIA PhysX engine to model AGV dynamics—including payload variation, wheel–ground interactions, and suspension effects—addressing a critical gap in surveyed studies. A factory-floor virtual environment was developed, and a holonomic AGV was implemented with RigidBody and WheelCollider components. PID controllers were tuned via Exhaustive Search and Ziegler–Nichols methods across loads from 0 kg to 100 kg. Exhaustive Search achieved a mean lateral error of just 0.0069 cm and a standard deviation of 1.33 cm at 50 kg—58% lower variability than Ziegler–Nichols. Meanwhile, controller tuning using Ziegler–Nichols required only up to 40 min per load but exhibited up to 84% inter-operator gain variability. Performance was validated on infinity-shaped track, demonstrating Unity’s utility for quantitative performance benchmarking. As contributions, this study (i) presents a novel dynamic AGV simulation framework, (ii) proposes a dual validation workflow combining on-site tuning and systematic optimization, and (iii) integrates an embedded evaluation suite for reproducible control- strategy comparisons. Full article

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions. Full article

Efficient autonomous exploration in unknown obstacle cluttered environments with interior obstacles remains a challenging task for mobile robots. In this work, we present a novel exploration process for a non-holonomic agent exploring 2D spaces using onboard LiDAR sensing. The proposed method generates velocity commands based on the calculation of the solution of an elliptic Partial Differential Equation with Dirichlet boundary conditions. While solving Laplace’s equation yields collision-free motion towards the free space boundary, the agent may become trapped in regions distant from free frontiers, where the potential field becomes almost flat, and consequently the agent’s velocity nullifies as the gradient vanishes. To address this, we solve a Poisson equation, introducing a source point on the free explored boundary which is located at the closest point from the agent and attracts it towards unexplored regions. The source values are determined by an exponential function based on the shortest path of a Hybrid Visibility Graph, a graph that models the explored space and connects obstacle regions via minimum-length edges. The computational process we apply is based on the Walking on Sphere algorithm, a method that employs Brownian motion and Monte Carlo Integration and ensures efficient calculation. We validate the approach using a real-world platform; an AmigoBot equipped with a LiDAR sensor, controlled via a ROS-MATLAB interface. Experimental results demonstrate that the proposed method provides smooth and deadlock-free navigation in complex, cluttered environments, highlighting its potential for robust autonomous exploration in unknown indoor spaces. Full article

Path planning algorithms for mobile robots and autonomous systems have advanced considerably, yet challenges remain in navigating complex environments while satisfying non-holonomic constraints and achieving precise target orientation. Phase portraits are traditionally used to analyse dynamical systems via equilibrium points and system trajectories, and can be a powerful framework for addressing these challenges. In this work, we propose a novel orientation-aware path planning algorithm that uses phase portrait dynamics by treating both obstacles and target poses as equilibrium points within the environment. Unlike conventional approaches, our method explicitly incorporates non-holonomic constraints and target orientation requirements, resulting in smooth, feasible trajectories with high final pose accuracy. Simulation results across 28 diverse scenarios show that our method achieves zero final orientation error with path lengths comparable to Hybrid A*, and planning times reduced by $52\%$ on the indoor map and $84\%$ on the playpen map relative to Hybrid A*. These results highlight the potential of phase portrait-based planning as an effective and efficient method for real-time autonomous navigation. Full article

This paper proposes a novel approach to the virtual 3D modeling of articulated mechanisms. It follows the widespread use of XML (eXtensible Markup Language) for various applications and defines a version of XML that is specially designed for the description of 3D geometric models of articulated bodies. In addition, it shows how the 3D geometric model of a mechanism can be gradually developed through the use of suitably defined elements and stored in a corresponding XML file. The developed XML model is processed, and using a powerful VTK (Visualization Toolkit) library, the corresponding virtual model is built and shown on the computer screen. To drive the virtual model, the dynamic model of the mechanism is developed using Bond Graph modeling techniques. Virtual 3D geometric and dynamic models are created using the corresponding software packages: BonSim3D 2023 Visual and BondSim 2023. The models are interconnected by a two-way named pipe. During the simulation of the dynamic model, the parameters necessary to drive the virtual model (e.g., the joint displacements) are collected and sent to the virtual model over the pipe. When the virtual model receives a package, the computer screen is updated by showing the new state of the mechanism. The approach is demonstrated using the example of a holonomic omnidirectional mobile robot. Full article

This paper presents an obstacle avoidance strategy for mobile manipulators consisting of a robotic arm and a base with a non-holonomic differential wheel system. The algorithm makes it possible to avoid obstacles in a dynamic environment, without planning the path a priori. A series of examples are proposed in simulation using Matlab and analyzed to show how the algorithm works if the obstacle interferes with the manipulator or the base. In addition, the possibility of prioritizing the movement of certain parts of the system using the weighted pseudo-inverse matrix is introduced. In this way, it is possible to give movement priority to the base if it is necessary to move the robot over long distances while keeping the manipulator as still as possible. The use of null space to keep the end-effector stationary while it avoids obstacles is also explored, exploiting the system’s redundancy and allowing the rest of the kinematic chain and the mobile base to move accordingly. Finally, current standards are analyzed and a solution is shown that allows the robot to vary its behavior to avoid obstacles depending on the distance to the target point. Full article

Velocity saturation constraints are a significant issue for wheeled mobile robots (WMRs) when designing kinematics-based control laws. To handle the problem of velocity saturation constraints, a novel monocular visual servoing controller is developed for WMRs to solve tracking problems and enable unknown depth estimation. By analyzing the kinematic model of the robot system and employing the homography decomposition technique, measurable signals are obtained to develop a visual tracking error model for non-holonomic mobile robots. To ensure that the velocity commands are consistently constrained within the allowed limits, a saturation function is employed in the designed visual servoing control law. Furthermore, an adaptive updating law is designed to estimate the unknown depth information. The boundedness of the velocity commands is analyzed to evaluate the saturation performance of the developed visual servoing controller. With the aid of Lyapunov techniques and Barbalat’s lemma, the stability of this scheme is demonstrated. The simulation and experiment verify the performance of the proposed method. Full article

Mobile robots represent one of the most relevant areas of study within robotics due to their potential for designing and developing new nonlinear control structures that can be implemented in simulations and applications in specific environments. In this work, a fuzzy steering controller with a symmetric distribution of fuzzy numbers is proposed and designed for implementation in the kinematic model of a non-holonomic mobile robot. The symmetry in the distribution of triangular fuzzy numbers contributes to a balanced response to disturbances and minimizes systematic errors in direction estimation. Additionally, it improves the system’s adaptability to various reference paths, ensuring accurate tracking and optimized performance in robot navigation. Furthermore, this fuzzy logic-based controller emulates the behavior of a classic PID controller by offering a robust and flexible alternative to traditional methods. A virtual environment was also developed using the UNITY platform to evaluate the performance of the fuzzy controller. The results were evaluated by considering the average tracking error, maximum error, steady-state error, settling time, and total distance traveled, emphasizing the trajectory error. The circular trajectory showed high accuracy with an average error of 0.0089 m, while the cross trajectory presented 0.01814 m, reflecting slight deviations in the turns. The point-to-point trajectory registered a more significant error of 0.9531 m due to abrupt transitions, although with effective corrections in a steady state. The simulation results validate the robustness of the proposed fuzzy controller, providing quantitative insights into its precision and efficiency in a virtual environment, and demonstrating the effectiveness of the proposal. Full article

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications. Full article

This work concerns the autonomous navigation of non-holonomic ground mobile robots in a GPS-denied environment. The objective was to locate, in a global frame, without GPS, anonymous ground mobile robots starting from two consecutive aerial images captured by a single fixed webcam. The effectiveness of deep learning by a MultiLayer Perceptron in an indexed localization was compared to the methods studied in previous works. The ability of a robot to determine the position of other non-indexed robots was also performed. The structure and parameters of the network and the choice of the points taken into account during the learning phase to obtain a local optimum are presented. The results, obtained from simulated and experimental data, are compared to those obtained with more classical methods for different sampling periods (time between images). Full article