Search Results (76)

Safety is a fundamental issue in autonomous mobile robot design. This paper presents a safety-critical control framework for nonholonomic tractor-trailer vehicles subject to both safety and physical constraints. Control barrier functions (CBFs) are employed to enforce hard safety constraints, generating a safe set that ensures obstacle avoidance. These CBFs are integrated with control Lyapunov functions (CLFs) to enable trajectory tracking while maintaining safety. To reduce conflicts between CBFs and CLFs and improve obstacle avoidance performance, we optimize their decay rates. Leveraging the concept of differential flatness, we design a controller that enables the system to follow the desired safe trajectory. Finally, the effectiveness and performance of the proposed method are demonstrated through numerical simulations and real-world experiments. Full article

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, such as unexpected inertial stress and strain on the mechanical structure, passenger discomfort, and unsafe and unpredictable deviation of the real trajectory with respect to the reference planned one. Oppositely, smooth and feasible trajectories are often desired in real-time navigation for nonholonomic mobile robots where the surrounding environment can have a dynamic and complex shape with obstacles. In this paper, we propose a novel technique for the generation of smooth, collision-free, and near time-optimal paths for nonholonomic mobile robots. The proposed method exploits the features of a set of tunable bump functions, with the goal of pursuing smooth reference curves with tunable features (such as curvature, or jerk) yet seeking a reasonable length minimality, thus combining the advantages of the two most adopted techniques, namely Bezier interpolation and Dubins curves. After a thorough description of the analytical methods, the paper is primarily concerned with the design and tuning methods of the path-planning algorithm. Both a graphical method and numerical investigations and examples are performed to fully exploit the algorithm potentialities and to show the efficiency of the proposed strategy. 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

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

The widespread application of multi-agent robotic systems in domains such as agricultural collaboration and automation has accentuated the challenges faced in seeking to achieve rapid synchronization and sustain high-performance control under conditions where velocity states remain unmeasurable. To relieve these challenges, a synchronization control framework is proposed for multi-agent systems, employing non-uniform sampling communication protocols. Initially, a state-variable transformation is applied to construct a composite Lyapunov function that integrates a sampling term. An explicit relation is then derived between the communication interval and the global exponential synchronization rate, thereby establishing a theoretical foundation for the design of non-periodic sampling-based control strategies. Second, a linear-state feedback controller is introduced, which balances convergence speed with the limited frequency of information updates, ensuring asymptotic stability even under prolonged sampling intervals. Third, a velocity observer was designed based on Immersion and Invariance ($I\&I$) theory to solve the problem of unmeasurable velocity states, ensuring the exponential convergence of the estimation error. Finally, the simulation results demonstrate that, with sampling intervals of $h\in [0.03,0.08]$ s, the position errors $\parallel q_i-q_{d,i}\parallel$ of all six robots converge to below ${10}^{-2}$ within 7 s; meanwhile, the velocity estimation errors decay to nearly zero within 7 s, confirming the effectiveness of the proposed method. The main contributions of this work can be summarized as follows: (1) a new $I\&I$ velocity observer is tailored for discrete-time communication; (2) rigorous proof of global exponential convergence is provided via a composite Lyapunov energy function; (3) a reproducible MATLAB simulation framework is presented that enhances both the verifiability and applicability of the proposed approach. Full article

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. 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 study aims to explore the tracking control challenge in a swarm of multiple nonholonomic wheeled mobile robots (NWMRs) by utilizing a distributed leader–follower strategy grounded in the cascade system theory. Firstly, the kinematic control law is developed for the leader by constructing a sliding surface based on the error tracking model with a virtual reference trajectory. Secondly, a communication topology with the desired formation pattern is modeled for the multiple robots by using the graph theory. Further, in the leader–follower NWMR system, each follower lacks direct access to the leader’s information. Therefore, a novel distributed-based controller by PD-based controller for the follower is developed, enabling each follower to obtain the leader’s information. Thirdly, for each case, we give a further analysis of the closed-loop system to guarantee uniform global asymptotic stability with the conditions based on the cascade system theory. Finally, the trajectory tracking performance of the proposed controllers for the NWMR system is illustrated through simulation results. The leader robot achieved a low RMSE of 1.6572 (Robot 1), indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.6425 (Robot 2), 3.0132 (Robot 3), and 4.2132 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances. Full article

Nonholonomoic mobile robots (NMRs) are widely used in logistics transportation and industrial production, with motion control remaining a key focus in current WMR research. However, most previously developed controllers assume ideal conditions without considering motion slippage. Neglecting slippage factors often leads to reduced control performance, causing instability and deviation from the robot’s path. To address such a challenge, this paper proposes an intelligent method for estimating the longitudinal wheel slip, enabling effective compensation for the adverse effects of slippage. The proposed algorithm relies on the development of an adaptive trajectory tracking controller for the leader robot. This controller enables the leader robot to accurately follow a virtual reference trajectory while estimating the actual slipping ratio with precision. By employing this approach, the mobile robot can effectively address the challenge of wheel slipping and enhance its overall performance. Next, a distributed observer is developed for each NMR that uses both its own and adjacent robot’s information to determine the leader’s state. To solve this difficulty for the follower robot to receive the states of the leader in a large group of robots, distributed formation controllers are designed. Further, Lyapunov stability theory is utilized to analyze the convergence of tracking errors that guarantees multi-robot formation. At last, numerical simulations on a group of NMR are provided to illustrate the performance of the designed controller. The leader robot achieved a low RMSE of 1.7571, indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.7405 (Robot 2), 3.0789 (Robot 4), and 4.3065 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances. 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

Autonomous navigation in indoor environments demands reliable perception and control strategies for nonholonomic mobile robots operating under geometric constraints. While visual servoing offers a promising framework for such tasks, conventional approaches often rely on explicit 3D feature estimation or predefined reference trajectories, limiting their adaptability in dynamic scenarios. In this paper, we propose a novel nonholonomic mobile robot corridor-following and doorway-passing method based on image-based visual servoing (IBVS) by using a single dioptric camera. Based on the unifying central spherical projection model, we present the projection mechanism of 3D lines and properties of line images for two 3D parallel lines under different robot poses. In the normalized image plane, we define a triangle enclosed by two polar lines in relation to line image conic features, and adopt a polar representation for visual features, which will naturally become zero when the robot follows the corridor middle line. The IBVS control law for the corridor-following task does not need to pre-calculate expected visual features or estimate the 3D information of image features, and is extended to doorway-passing by simply introducing an upper door frame to modify visual features for the control law. Simulations including straight corridor-following, anti-noise performance, convergence of the control law, doorway-passing, and loop-closed corridor-following are conducted. We develop a ROS-based IBVS system on our real robot platform; the experimental results validate that the proposed method is suitable for the autonomous indoor visual navigation control task for a nonholonomic mobile robot equipped with a single dioptric camera. Full article

This article delves into the problem of fuzzy adaptive event-triggered (ET) formation control for nonholonomic mobile robots (NMRs) subject to full-state constraints. Fuzzy logic systems (FLSs) are employed to identify the unknown nonlinear functions within the system. To guarantee that all system states remain within their constraint boundaries, barrier Lyapunov functions (BLFs) are meticulously constructed. Subsequently, within the framework of the backstepping control design algorithm, we propose a novel fuzzy adaptive ET formation controller. Our ET mechanism can achieve an overall resource-saving rate of 88.17% for the four robots. Rigorous theoretical analysis demonstrates that the designed strategy not only ensures the stability of the controlled NMRs but also enables the formation tracking errors to converge to a small neighborhood around zero. Notably, the BLFs-based control approach presented herein endows the system with the capacity to avoid collisions to a certain degree, enhancing the overall safety and reliability of the robot formation. Finally, a simulation example is provided. The results vividly illustrate the effectiveness and practicality of the proposed theory, validating its potential for real-world applications in the field of nonholonomic mobile robot formation control. 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