Trajectory Tracking Of Mobile Robot Research Articles

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

Safety-Critical Trajectory Tracking for Mobile Robots with Guaranteed Performance

Safety-Critical Trajectory Tracking for Mobile Robots with Guaranteed Performance

Trajectory Tracking of Wheeled Mobile Robot Through System Identification and Control Using Deep Neural Network

Trajectory Tracking of Wheeled Mobile Robot Through System Identification and Control Using Deep Neural Network

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

Correction: Trajectory Tracking of Differential Wheeled Mobile Robots with Input Saturation and Mismatched Centers

Correction: Trajectory Tracking of Differential Wheeled Mobile Robots with Input Saturation and Mismatched Centers

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

Visual Servoing Trajectory Tracking and Depth Identification for Mobile Robots With Velocity Saturation Constraints

Visual Servoing Trajectory Tracking and Depth Identification for Mobile Robots With Velocity Saturation Constraints

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

Trajectory tracking of a wheeled mobile robot based on the predefined‐time sliding mode control scheme

AbstractIn this paper, a double‐loop control method is proposed to improve the trajectory tracking performance of a wheeled mobile robot (WMR) with slippage properties and external disturbances. Considering the strong robustness and the ability to enable the system to converge in a short time, the predefined‐time sliding mode control (PTSMC) strategy is applied to the design of a double‐loop controller. Furthermore, a nonlinear disturbance observer (NDO) is adopted to comply with the feedforward compensation of the external disturbances. In turn, the controller only needs to deal with the internal disturbance of the system under the premise of using the NDO, thereby reducing the burden of the sliding mode controller. Based on Lyapunov methods, the stability of the double‐loop controller and the NDO is analyzed. Finally, the feasibility of the double‐loop control strategy is proven by simulations of the WMR operating along circular, sin‐shaped, and eight‐shaped roads.

Formation and Trajectory Tracking of Mobile Robots with Uncertainties and Disturbances Using an Adaptive Immune Fuzzy Quasi-Sliding Mode Control

Formation and Trajectory Tracking of Mobile Robots with Uncertainties and Disturbances Using an Adaptive Immune Fuzzy Quasi-Sliding Mode Control

GMPC: Geometric Model Predictive Control for Wheeled Mobile Robot Trajectory Tracking

GMPC: Geometric Model Predictive Control for Wheeled Mobile Robot Trajectory Tracking

Trajectory tracking of mobile robots using hedge-agebras-based controllers

Trajectory tracking of mobile robots using hedge-agebras-based controllers

Trajectory tracking for non-holonomic mobile robots: A comparison of sliding mode control approaches

This paper implements and compares the performance of three controllers based on Sliding Mode Control (SMC) applied to the motion control of mobile robots. The controllers include a standard SMC controller and two variations, namely Dynamic Sliding Mode Control (D-SMC) and Dual Sliding Mode Control (DM-SMC). The three controllers differ from conventional SMC approaches since they are designed based on a generic reduced-order empirical model that can represent any system that exhibits behavior akin to a First-Order Plus Delay Time (FOPDT) system. In this study, the comparison of controllers focuses on the Trajectory Tracking Problem (TTP) of a Nonholonomic Mobile Robot (NMR). Performance indices are employed to quantify the controllers' results across three trajectory types. Simulation results evidence that if a smooth curvature trajectory is tracked, the best performance (in terms of minimizing trajectory tracking error and controller effort) can be obtained by using a DM-SMC. On the other hand, if there are discontinuities in the curvature, the results suggest that a better alternative is the D-SMC since it compensates for abrupt changes as in the case of tracking a square trajectory. Overall, the findings from this work demonstrated that the use of simplified empirical models to design SMC-based motion controllers can be successfully applied to solve the TTP of NMR.

Observer-based finite-time control for trajectory tracking of wheeled mobile robots with kinematic disturbances

Wheeled Mobile Robots (WMRs) are systems with applications in diverse fields such as transportation, civilian services, military use, and space exploration. Then, their use will continue increasing, making WMRs an essential research topic that deserves further study. To this end, this work presents a novel observer-based finite-time controller for trajectory tracking control of WMRs disturbed by kinematic disturbances. In the proposed approach, the kinematic model of the WMR is transformed into a set of two decoupled second-order systems. Then, the proposed controller is divided into two parts. The first one employs an observer to estimate the effect of the kinematic disturbances. The second part consists of a finite-time controller designed to achieve finite-time convergence of the tracking error. A detailed synthesis procedure theoretically demonstrates the feasibility of the proposed controller. Subsequently, the proposed scheme is compared against finite-time, feedback, and H∞ controllers. Exhaustive numerical simulations show that the proposed new control methodology achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control procedures. Finally, some comments and numerical results are given to clarify how the proposed control methodology can be used to design new controllers for trajectory tracking in WMRs and demonstrate that the new proposal remains to have a good performance when the system’s coordinates are corrupted by measurement noise.

Trajectory Tracking of Differential Wheeled Mobile Robots with Input Saturation and Mismatched Centers

Trajectory Tracking of Differential Wheeled Mobile Robots with Input Saturation and Mismatched Centers

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement. Designing mobile robots that can be intelligently managed and operate autonomously when traveling from one place to another requires at least two steps. To start with, path planning is required to prevent motion collisions. Tracking the robot's trajectory is a crucial second task. The primary goal of this study is to find the quickest and safest path between the two positions. In this work, we investigated the path planning of a mobile robot with dynamic, and dynamic obstacles with moving goal environments using RRT, BiRRT, and HA* algorithms. These algorithms are easy, computationally inexpensive, and simple to use. They have been chosen for numerous real-time path-planning applications. The DDMR's kinematic model has been utilized in this paper to control path tracking, and a PID controller has been proposed to reduce tracking deviations between the robot's actual route and the reference trajectory. This work introduced the PSO, FPA, CSA, SSA, BWOA, and proposed HBPO optimization techniques for obtaining PID parameters (k_p,k_i,k_d) for improved mobile robot trajectory tracking. The simulation results have been examined using three trajectory shapes: step, circular, and infinite. The simulation findings reveal that HA* outperforms the other algorithms by generating collision-free pathways that are smoother and shorter than their RRT and BiRRT equivalents. On the other hand, the proposed HBPO outperforms the other methods. The HBPO method converges quicker than the other proposed algorithms.

Reinforcement Learning-Based Approach to Robot Path Tracking in Nonlinear Dynamic Environments

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system’s environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

AbstractTo promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error.