Search Results (41)

This paper proposes a hybrid SMC + ESO + RBF control architecture designed to evaluate trajectory tracking and liquid sloshing suppression in tanker robots navigating complex terrains within a simulated environment. A multi-variable dynamic model integrates the differential drive mobile platform with an equivalent mass-spring-damper sloshing system under terrain disturbances. To achieve robust stability, an Extended State Observer (ESO) neutralizes baseline generalized disturbances, while a Radial Basis Function (RBF) neural network adaptively compensates for residual nonlinear coupled sloshing errors. Practical stability and uniform ultimate boundedness (UUB) of the closed-loop system are proven via Lyapunov theory under bounded network approximation errors and observer uncertainties. Numerical simulations in MATLAB/Simulink demonstrate that the proposed controller achieves a baseline Root Mean Square Error (RMSE) of 0.0109 m, representing an 84.1% improvement over traditional Sliding Mode Control (SMC). Parametric sensitivity analysis under variable liquid filling ratios (30%, 50%, and 70%) and a circular steering topology indicates notable adaptability, with the tracking RMSE bounded between 0.0085 m and 0.0129 m under the considered virtual scenarios. Within the simulated environment, the system successfully smooths control profiles and dampens liquid oscillations, demonstrating a promising potential to support transport safety and mitigate actuator chattering under virtual constraints. However, these qualitative observations serve as preliminary hypotheses and must be formally verified through future hardware-in-the-loop (HIL) experiments to evaluate the impact of physical non-idealities, including sensor noise, actuator saturation, communication delays, and wheel slip. These findings confirm the competitive analytical robustness of the SMC + ESO + RBF framework in stabilizing tanker robots within highly uncertain simulated operational 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

This article investigates the trajectory tracking control of a differential-drive two-wheeled mobile robot (DDWMR) using its kinematic model, framed in the context of sustainability. A nonlinear-to-linear transformation based on differential flatness is employed to convert the original nonlinear system into two fully decoupled linear subsystems, enabling a simple and robust controller design. Unlike conventional flatness-based methods that rely on exact feedforward linearization around a reference trajectory, the proposed approach performs plant linearization, ensuring reliable tracking across a wide range of trajectories. The resulting two-loop architecture consists of an inner nonlinear loop implementing state prolongation and static feedback and an outer linear controller performing trajectory tracking of the linearized system. Simulation results on a circular reference trajectory demonstrate high tracking accuracy, with a maximum transient deviation of 0.28 m, a settling time of approximately 120 s, and a steady-state mean tracking error below 0.01 m. These results confirm that the plant-linearization-based framework provides accuracy, robustness, and practical applicability for DDWMR trajectory tracking, all within a responsible control environment. Full article

Autonomous navigation is a key requirement for underground mine automation, where the choice of a suitable global path planner plays a significant role. In this study, four representative planning approaches—Dijkstra’s algorithm, A*, Rapidly exploring Random Tree (RRT*), and Particle Swarm Optimization (PSO)—were evaluated on a differential-drive mobile robot within the ROS navigation framework. The algorithms were tested in two simulated underground environments: a room-and-pillar layout with relatively open space and multiple path alternatives and a narrow tunnel scenario designed to reflect more constrained mining conditions. The results indicate that Dijkstra’s algorithm consistently produced the shortest paths with the lowest computation times, while A* showed comparable performance with slightly higher computational effort. RRT* required modifications to operate effectively in narrow tunnels and exhibited significantly longer planning times. PSO, although capable of generating near-optimal solutions in open spaces, showed limitations in constrained environments due to collision handling and path feasibility issues. Differences in replanning behavior were observed when unknown obstacles were introduced. Overall, graph-based planners such as A* and Dijkstra’s algorithm demonstrated more stable and predictable performance. Future work will focus on validating these findings in real mining environments, particularly considering wheel slippage, sensor noise, and path generation challenges in narrow tunnel conditions. Full article

This paper addresses global path planning for a wheeled mobile robot with two different kinematic structures, considering both shortest path and minimum energy consumption criteria. The main research question concerns how the robot’s kinematic structure and the selected planning algorithm influence the resulting path with respect to these criteria. Our review of the state of the art discusses selected path planning methods, including model-based approaches. To determine the energy optimal path, a simplified model of the PIAP GRANITE robot was developed. The robot can be configured as either differentially driven or skid-steered. In the differentially driven configuration, the robot has two driven wheels and two caster wheels, whereas in the skid-steered configuration all wheels are independently driven. The robot’s models are based on previous theoretical and experimental studies and include kinematics, dynamics, drive units, and wheel slip phenomena. For path planning, it was assumed that the robot can move straight or turn. A flat terrain representative of typical urban environments was modeled as a grid of square cells, each characterized by friction and rolling resistance coefficients. Path planning was performed using A*, Theta*, and RRT* algorithms. In order to quantitatively evaluate the results, quality indexes were defined, including path length, energy consumption, computation time, and the number of analyzed nodes. Simulation results are presented for selected terrain maps, both robot configurations, all algorithms, and both optimization criteria. The results show that the differentially driven configuration is consistently more energy-efficient. For the skid-steered robot, minimizing the number of turns is crucial due to high turning energy costs. The A* algorithm consistently finds optimal paths, whereas RRT* is faster but produces non-optimal and non-repeatable results. Theta* does not always achieve optimality due to limitations imposed by the line-of-sight function. 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

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article

The increasing inclusion of robots in social areas requires continuous improvement of behavioral strategies that robots must follow. Although behavioral strategies mainly focus on operational efficiency, other aspects should be considered to provide a reliable interaction in terms of sociability (e.g., methods for detection and interpretation of human behaviors, how and where human–robot interaction is performed, and participant evaluation of robot behavior). This scoping review aims to answer seven research questions related to robotic motion in socially aware navigation, considering some aspects such as: type of robots used, characteristics, and type of sensors used to detect human behavioral cues, type of environment, and situations. Articles were collected on the ACM Digital Library, Emerald Insight, IEEE Xplore, ScienceDirect, MDPI, and SpringerLink databases. The PRISMA-ScR protocol was used to conduct the searches. Selected articles met the following inclusion criteria. They: (1) were published between January 2018 and August 2025, (2) were written in English, (3) were published in journals or conference proceedings, (4) focused on social robots, (5) addressed Socially Aware Navigation (SAN), and (6) involved the participation of volunteers in experiments. As a result, 22 studies were included; 77.27% of them employed mobile wheeled robots. Platforms using differential and omnidirectional drive systems were each used in 36.36% of the articles. 50% of the studies used a functional robot appearance, in contrast to bio-inspired appearances used in 31.80% of the cases. Among the frequency of sensors used to collect data from participants, vision-based technologies were the most used (with monocular cameras and 3D-vision systems each reported in 7 articles). Processing was mainly performed on board (50%) of the robot. A total of 59.1% of the studies were performed in real-world environments rather than simulations (36.36%), and a few studies were performed in hybrid environments (4.54%). Robot interactive behaviors were identified in different experiments: physical behaviors were present in all experiments. A few studies employed visual behaviors (2 times). In just over half of the studies (13 studies), participants were asked to provide post-experiment feedback. Full article

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations—pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks. Yet, its performance relies on manually tuned parameters and lacks adaptability to dynamic changes. This study introduces a hybrid grey wolf-particle swarm optimization (GPSO) algorithm, combining the exploratory ability of a grey wolf optimizer with the rapid convergence of particle swarm optimization. The GPSO algorithm adaptively tunes MPC parameters online to improve control. A kinematic model of a four-wheeled differential-drive robot is developed, and an MPC controller using error-state linearization is implemented. GPSO integrates hierarchical leadership and chaotic disturbance strategies to enhance global search and local convergence. Simulation experiments on circular and double-lane-change trajectories show that GPSO-MPC outperforms standard MPC and PSO-MPC in tracking accuracy, heading stability, and control smoothness. The results confirm the improved adaptability and robustness of the proposed method, supporting its effectiveness in dynamic underwater environments. Full article

This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system’s effectiveness and suitability for real-time mobile robot navigation in indoor environments. Full article

Navigation is a fundamental technology for mobile robots. However, many trajectory planning methods suffer from curvature discontinuities, leading to instability during robot operation. To address this challenge, this paper proposes a navigation scheme that adheres to the kinematic constraints of a two-wheeled differential-drive robot. An improved and efficient RRT algorithm is employed for global navigation, while an adaptive clothoid curve is utilized for local trajectory smoothing. Simulation results demonstrate that the proposed method effectively eliminates curvature discontinuities and enhances operational efficiency. Full article

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

This paper is devoted to the solution of the optimal control problem. The obtained control should be optimal in terms of quality criteria and, at the same time, feasible when implemented in the control object. To solve the optimal control problem in the class of feasible control functions, an advanced mathematical model of the control object is used. Firstly, the universal stabilisation system of the motion along any trajectory from some class is developed via symbolic regression. Then, the obtained stabilisation system is inserted into the right part of the control object model instead of the control vector. A reference model with a free control vector in the right part is added to the model; thus, the advanced mathematical model of the control object is obtained. After this, the optimal control problem is solved with the advanced mathematical model of the control object. The optimal control problem is stated in the classical form when the control is a time function. Here, the control function is searched for the reference model. The preliminary design of the universal stabilisation system for some class of trajectories allows the solution of the optimal control problem via the control object in a reasonable time frame. The proposed methodology is computationally tested for a model of the spatial motion of a quadcopter and a group of two-wheeled mobile robots with a differential drive. The results of the experiments show that the universal stabilisation system ensures the stabilisation of the motion of the objects along optimal trajectories, which are not known beforehand but obtained as a result of solving the problem with an advanced model. Full article

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments. The fusion filter gains in FIS-IESKF are adaptively adjusted for noise predictions, optimizing the rule parameters of the fuzzy inference process. Experimental results demonstrate that the proposed method effectively enhances the localization accuracy and system robustness of differential-drive indoor mobile robots in dynamically changing movements and environments. Full article