Visual Servoing Trajectory Tracking of Nonholonomic Mobile Robots Without Direct Position Measurement

Compared to four-wheel independent drive and steering (4WID4WIS) mobile robots, three-wheel independent drive and steering (3WID3WIS) mobile robots are more cost-effective due to their lower cost, lighter weight, and better handling performance, even though their acceleration performance is reduced. This paper proposes a dynamic model predictive control (DMPC) controller for trajectory tracking of 3WID3WIS mobile robots to simplify the computational complexity and improve the accuracy of traditional model predictive control (MPC). The A* algorithm with a non-point mass model is used for path planning, enabling the robot to navigate quickly in narrow and constrained environments. Firstly, the kinematic model of the 3WID3WIS mobile robot is established. Then, based on this model, a DMPC trajectory tracking controller with dynamic effects is developed. By replacing MPC with DMPC, the computational complexity of MPC is reduced. During each control period, the prediction horizon is dynamically adjusted based on changes in trajectory curvature, establishing a functional relationship between trajectory curvature and prediction horizon. Subsequently, a comparative study between the proposed controller and the traditional MPC controller is conducted. Finally, the new controller is applied to address the trajectory tracking problem of the 3WID3WIS mobile robot. The experimental results show that DMPC improves the lateral trajectory tracking accuracy by 62.94% and the heading angle tracking accuracy by 34.81% compared to MPC.

As nonholonomic mobile robots have constraints imposed on motions that are not integrable, i.e. the constraints cannot be written as time derivatives of some function of the generalized coordinates, advanced techniques are needed for the tracking control. In the paper a robust control law is proposed for trajectory tracking of nonholonomic wheeled mobile robots. The state variables of the mobile robot are represented in polar coordinates, and the dynamic equation of the system is feedback-linearized by a computed-torque method. A novel sliding mode control law is derived for asymptotically stabilizing the mobile robot to a desired trajectory. It is shown that the proposed scheme is robust to bounded system disturbances. Simulation examples and experimental results are provided to show the effectiveness of the accurate tracking capability and the robust performance of the proposed controller.

This study aims to solve the issues of nonlinearity, non-integrity constraints, under-actuated systems in mobile robots. The wheeled robot is selected as the research object, and a kinematic and dynamic control model based on Internet of Things (IoT) and neural network is proposed. With the help of IoT sensors, the proposed model can realize effective control of the mobile robot under the premise of ensuring safety using the model tracking scheme and the radial basis function adaptive control algorithm. The results show that the robot can be controlled effectively to break the speed and acceleration constraints using the strategy based on the model predictive control, thus realizing smooth movement under the premise of safety. The self-adapting algorithm based on the IoT and neural network shows notable advantages in parameter uncertainty and roller skidding well. The proposed model algorithm shows a fast convergence rate of about 2 s, which has effectively improved performances in trajectory tracking and robustness of the wheeled mobile robot, and can solve the difficulties of wheeled mobile robots in practical applications, showing reliable reference value for algorithm research in this field.

Many researchers have become interested in wheeled mobile robot (WMR) trajectory tracking control in recent years. This is due to the increased application of mobile robots in the industry, the military, the home, and public service. Classically, the movement of WMR is controlled depending on its kinematic model. However, in real-time applications, both the dynamic and kinematic models of robots and external disturbance and uncertainty affect system performance. This paper proposes backstepping combined with a Nonlinear Proportional-Integral-Derivative (NPID) controller to control a two-wheeled mobile robot (TWMR). The kinematic and dynamic models of the WMR are derived. The dynamic modeling is derived using a Lagrangian approach, and stability of the system is achieved using the Lyapunov method. The controller gains are optimized using the Genetic Algorithm optimization technique. The proposed algorithms’ performance is tested using Matlab software. The simulation result shows that the proposed method achieved preferable reference trajectory tracking with a minimum tracking error. The proposed controller outperforms the GA-based backstepping plus PID controller in terms of root-mean-square (RMS) of trajectory tracking error (47.36% in a linear and 60.32% in a nonlinear case). In addition, it shows good unknown disturbance rejection and initial point change in all scenarios.

Motion estimation is one of the key perception problems for Micro Aerial Vehicles (MAVs). Recent developments based on visual-inertial sensors exhibit promising performance. In this paper, we propose a fast stereo-inertial odometry approach leveraging the advantages of both visual feature tracking and visual inertial fusion. Instead of tracking visual feature on every frame, our method estimates short-term motion with inertial-assisted flow tracking. By that, the frame-rate of visual feature detection and tracking is reduced to save computational power. Also, we design an Extended Kalman Filter (EKF) to achieve full state estimation, including position, velocity and attitude. Our method is able to estimate camera motion with less computational effort, while maintaining comparative state estimation performance. Experiments on the Euroc dataset which is captured onboard a MAV show the good performance of our method.

This paper proposes a backstepping controller design for trajectory tracking of unicycle-type mobile robots. The main object of the control algorithms developed is to design a robust output tracking controller. The design of the controller is based on the lyapunov theorem, kinematic tracking controller of an unicycle-like mobile robot is used to provides the desired values of the linear and angular velocities for the given trajectory. A Lyapunov-based stability analysis is presented to guarantee the robot stability of the tracking errors. Simulation and experimental results show the effectiveness of the proposed robust controller in term of accuracy and stability under different load conditions.

Spherical mobile robot (spherical robot in short) has remarkable stability, flexible motion as well as significant advantages in the exploration of unmanned environments when compared with traditional mobile robots. However, as a special under-actuated nonholonomic system spherical robot cannot be transformed to the classic chained form and is difficult to control. At present, there has not been a kinematics based trajectory tracking controller which could track both the position states and the attitude states of spherical robot simultaneously. This paper deduced a three states (two position states and one attitude state) trajectory tracking controller using sliding-mode control method and based on the kinematic model of a spherical mobile robot BHQ-1. The jittering on the sliding surface was reduced under the effect of a saturation function in the controller. The effectiveness of the proposed trajectory tracking controller were verified through MATLAB simulations for both a linear trajectory and a circular trajectory.

The key attributes of Two Wheeled Balancing Mobile Robots (TWBMRs) are nonholonomic constraints and inherent instability. This paper deals with the problem of balancing and trajectory tracking of TWBMR using backstepping Sliding Mode Controller (SMC). First, the mathematical representation of TWBMR is derived using Lagrangian method by incorporating the dynamics of DC motors. Then, a decoupling approach is applied for simplifying the dynamic equations. The backstepping SMC technique is finally adopted to achieve the balancing and trajectory tracking of the TWBMR, whereas both model uncertainties and exogenous disturbance are taken into account in the controller design methodology. In order to determine the velocity, the trajectory tracking is achieved by the kinematic control, which is a common backstepping controller. For the velocity convergence of TWBMR to the generated desired value, two SMCs are designed, in which the motors voltage are directly controlled as the control laws. Simplicity in practical implementation and control law, ability to overcome uncertainties and appropriate performance are the main advantages of the proposed controller. The effectiveness of the proposed controller is verified through simulation and experimental results.

Vision and odometry/attitude and heading reference system (AHRS, three-axial gyroscopes, accelerometers, and magnetometers) sensors fusion strategy is prevalent in the recent years for the robot localization, due to its low cost and effectiveness in global positioning system (GPS)-denied environments. In this paper, a new adaptive estimation algorithm is proposed to estimate the robot position by fusing the monocular vision and odometry/AHRS sensors, and utilizing the properties of perspective projection. By the new method, the robot can be localized in real time in the GPS-denied and mapless environments, and the localization results can be theoretically proved convergent to their real values. Compared to other methods, our algorithm is simple to implement and suitable for parallel processing. To achieve the real-time performance, the algorithm is implemented in parallel using graphics processing unit (GPU), and therefore, it can be easily integrated into mobile robots' tasks like navigation and motion control, which need the real-time localization information. Simulations and experiments were conducted to validate the good convergence and longtime robustness performances of the proposed real-time localization algorithm.

In last few years there has been a growing interest in trajectory tracking and mobile robot navigation. In this paper, an effective design of a PI (Proportional Integral) controller to resolve two tasks ‘going to target’ and ‘following a trajectory’ is developed. This controller is designed to adjust the motor velocities of our considered platform (Khepera II). The responses of the robot controller in navigation and tracking have been obtained and graphically presented. The effectiveness of the proposed methodology is discussed.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

In this paper, an improved sliding mode algorithm with global asymptotic stability is presented for tracking control of nonholonomic mobile robot. Centroid offset parameters are used in the design of the controller, which can drive the position error to zero quickly in the premise of guaranteeing stability of the whole system. The Kinematics mathematical model and the position and posture error model of the mobile robot are presented. Switch function of variable structure control is designed based on the backstepping method. Comparative experiments are given and simulation results show that the proposed control method is effective and has better control performances.

In this paper, a trajectory tracking control for a non-holonomic differential wheeled mobile robot (WMR) system is presented. A big number of investigations have been used the kinematic model of mobile robot which is a nonlinear model in nature, thus a hard task to control it. This work focuses on the design of fuzzy PID controller tuned with a firefly optimization algorithm for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller's parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with the least possible value of error. Matlab Simulation results show that a good performance and robustness of the controller. This is confirmed by the value of minimized tracking error and the smooth velocity especially concerning presence of external disturbance or change in initial position of mobile robot.

Trajectory tracking and stabilization of two-wheeled balancing mobile robot with hierarchical and sliding mode control

This paper proposes a global integral sliding mode control (GISMC) scheme integrated with an improved disturbance observer (IDO) for robust trajectory tracking of nonholonomic wheeled mobile robots under uncertainties, disturbances, wheel slippage, and actuator faults. The NWMR dynamics are first formulated, followed by the development of the IDO to estimate and compensate for lumped disturbances in real time. The GISMC framework is then designed to ensure finite-time convergence and robustness, while a prescribed performance function (PPF) is incorporated to constrain the tracking errors within predefined bounds and guarantee system stability under dynamic conditions. Experiment results confirm that the proposed GISMC maintains tracking errors within ±0.012 m significantly smaller than those of the backstepping–adaptive sliding mode control (≈0.043 m) and achieves faster convergence with smoother control action than the backstepping–SMC with IDO. The results validate that the proposed controller adapts effectively in real time, providing enhanced robustness and precision in complex and uncertain environments. These findings highlight the potential of the proposed GISMC for practical applications in autonomous mobile systems requiring high accuracy and fault tolerance.