Autonomous Wheeled Mobile Robots Research Articles

Data‐based learning control for optimization of nonlinear systems

Data‐based learning control for optimization of nonlinear systems

Design of Path Planning Controller of Autonomous Wheeled Mobile Robot Based on Triple Pendulum Behaviour

Design of Path Planning Controller of Autonomous Wheeled Mobile Robot Based on Triple Pendulum Behaviour

Dual heuristic programming with just‐in‐time modeling for self‐learning fault‐tolerant control of mobile robots

AbstractThe development of autonomous wheeled mobile robots (WMRs) requires designing high‐performance motion controllers under complex dynamical constraints, which has been an important research topic in the fields of control theory and robotics. Considering the faults or disturbances in practice, previous works have designed various stable fault‐tolerant tracking control methods for WMRs. However, it is still challenging to design an optimal or nearly optimal controller in the case of faults. In this article, in order to deal with the changed properties of WMR dynamics, a just‐in‐time learning (JITL) based dual heuristic programming (DHP) algorithm is proposed to optimize the control performance under faults or disturbances. Without knowing the information of faults and the dynamics model, the changing dynamics can be identified online using the data‐driven method, and the control performance is optimized via DHP, which is a class of online reinforcement learning approach. The effectiveness of the proposed method is verified through fault‐tolerant tracking control simulations of a WMR system under different fault modes. It is shown that the proposed JITL‐based DHP approach has much better performance than previous DHP methods without fault estimation.

An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots

In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.

Autonomous wheeled mobile robot maneuvering in constraint environment. Trajectory tracking quality criteria

In this article the problem of control system synthesis for differential drive wheeled mobile robots operating within constraint environment is considered. The proposed approaches of solving trajectory tracking problem take into account the geometric features of both the robot and the transported cargo. The collision avoidance problem is solved at the controller level, without any changes to the planned trajectory. Three approaches are proposed: minimizing the distance to the trajectory, minimizing the distance to the reference position, and maximizing the distance to obstacles. In these approaches the constant analysis of position of each point of robot’s body outline contour is used. Negative influence of the position of these points on qualitative trajectory pass is represented in vector form. For each of the approaches formalized tracking quality criteria are determined, methods of implementation of control systems and controllers taking into account these criteria are proposed. The part of the control signal responsible for taking into account geometry of the robot and avoiding collisions is defined via the force-moment vector representation. Results of simulation modeling prove the efficiency of the proposed approaches. A comparison of the proposed approaches is presented. Potential ways of more efficient use of these approaches are given.

Simpsons 1/3 base fuzzy neural network with passive controller of wheeled mobile robot system

The paper investigates Passivity of Simpson 1/3 rule fuzzy neural network controller by teleoperation-based wheeled mobile robot navigation and wheel slippage. To render the obstacle avoidance for wheeled mobile robot and ensuring incident-free navigation particularly unlimited workspace and surface slippage of coordination for master robot position and slave robot velocity, a passivity controller mode is employed. Effective control strategies are formulated to achieve system stability in which soft-computing methodology is accessed to bypass robot wheel slippage and skidding. Soft-computing based fuzzy neural network system is framed through Simpson 1/3 rule for reducing master/slave robot position error on behalf of unavoidable and acceptable forces, which is confirmed by teleoperation system. We concluded mathematically, the system stability which has been shown via its passivity of the fuzzy neural network controller. The forces which can be handled by the human operator are approximately equal to the forces applied by the environmental force and sensor predictor of slave robot whichever comes under the unlimited workspace. Simulation results are verified with the effect of the proposed controller. Index Terms: Autonomous wheeled mobile robot, Passivity, adaptive network fuzzy inference system, Simpson’s 1/3 rule, teleoperation system.

Trajectory Planning For Autonomous Wheeled Mobile Robots With Trailer

Abstract This paper presents a dynamic optimization scheme for generating trajectory planning for autonomous wheeled mobile robots with tractor designed to accomplish missions in indoor environments. Such an optimization criterion problem requires a method that can yield a fast execution time and minimum traveling distance that contains geometrics, kinematics, and physical/environment constraints. The main goal is to develop optimal trajectories planning approach of an autonomous wheeled mobile robots with trailer for the execution of predefined tasks in structures environment. The developed approach can be considered as an extension of the Random Profiles Approach used for wheeled mobile robots. The results also illustrate that thanks to its time optimal trajectories planning, our scheme is well adapted to complex tasks as it can get shorter execution time for the autonomous holonomic tractor with a nonholonomic trailer.

Adaptive neuro-fuzzy inference system based on-the-move terrain classification for autonomous wheeled mobile robots

Building intelligence in autonomous robots to classify heterogeneous terrains on-the-move is a challenging task, but a pivotal feature required for accomplishing safety critical missions. This paper proposes an adaptive neuro-fuzzy inference system for online terrain classification in the wheeled mobile robot using the steady-state behaviour of robot wheel on the terrain. The key idea is to model the wheel-terrain interactions as a parametric varying system, whose steady-state behaviours are characterised by the terrain type. The proposed method uses the steady state gains and the corresponding input command to robot wheel for identifying the terrain type. Our results show that the proposed approach has a classification accuracy of 95.2% for the trained terrains, whereas 94.2% and 93.8% are observed in robust and adaptive testing, respectively. Additionally, a customised graphical user interface is developed to provide easy access to the researchers for terrain identification.

Adaptive neuro-fuzzy inference system based on-the-move terrain classification for autonomous wheeled mobile robots

Adaptive neuro-fuzzy inference system based on-the-move terrain classification for autonomous wheeled mobile robots

Design Interval Type-2 Fuzzy Like (PID) Controller for Trajectory Tracking of Mobile Robot

One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory tracking control of autonomous wheeled mobile robot in a changing unstructured environment needs to take into account different types of uncertainties. Type-1 fuzzy logic sets present limitations in handling those uncertainties while type-2 fuzzy logic sets can manage these uncertainties to give a superior performance. This paper focuses on the design of interval type-2 fuzzy like proportional-integral-derivative (PID) controller 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 minimum tracking error. The Matlab simulation results demonstrate the good performance and robustness of this controller. These were confirmed by the obtained values of the position tracking errors and a very smooth velocity, especially with regards to the presence of external disturbance or change in the initial position of mobile robot. Finally, in comparison with other proposed controllers, the results of nonlinear IT2FLC PID controller outperform the nonlinear PID neural controller in minimizing the MSE for all control variables and in the robustness measure.

Control synthesis for 4WS vehicle-robot model for traffic program motion

The object of research is an autonomous wheeled mobile robot model 4WS (Four Wheel Steering). The need for such studies is dictated by the limitations of using 2WS (Two Wheel Steering) robots to solve the problem of achieving multiple goals associated with insufficient maneuverability and traffic safety. This is one of the most problematic places of this model.Such a task was successfully solved to achieve a single goal with this model, including reverse movements for an articulated crew, but the nature of the trajectory with multiple goals makes such a task practically unsolvable. To solve it, the design of the autonomous mobile robot DDMR model was successfully applied. The advantages of the 4WS model compared to the 2WS in terms of increasing maneuverability have led to the study of the possibility of using it to solve this problem.In this study, the possibility of synthesizing the controlled movement of an autonomous mobile robot of the 4WS model along a program path specified in an explicit, implicit, parametric form or by the law of change in its curvature is realized. In this case, the angle of rotation of the front wheels is a function of the curvature of the program path, and the rear wheels are a function of the angle of rotation of the front. A feature of synthesized control for the 4WS model is the connection with control for the 2WS model.Control for this model is synthesized initially and is an independent value. Such a reference control: the empirical dependence determines the virtual radius for which such a control is calculated for the 4WS model so that it moves along the path of the 2WS model. The rotation of the rear wheels (in phase with the front or out of phase) is considered in this case as an additional control.An important feature of the study was the development of software that made it possible to perform numerical modeling of the synthesized control in the Maple mathematical package and visualization of maneuvers of movement in the Unity 3D system.The results of numerical modeling and their visualization allow to conclude that it is possible to use the synthesized law to control autonomous mobile robots created using the 4WS model.

An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM

Extended Kalman Filter (EKF) has been popularly utilized for solving Simultaneous Localization and Mapping (SLAM) problem. Essentially, it requires the accurate system model and known noise statistic. Nevertheless, this condition can be satisfied in simulation case. Hence, EKF has to be enhanced when it is applied in the real-application. Mainly, this improvement is known as adaptive-based approach. In many different cases, it is indicated by some manners of estimating for either part or full noise statistic. This paper present a proposed method based on the adaptive-based solution used for improving classical EKF namely An Adaptive Extended Kalman Filter. Initially, the classical EKF was improved based on Maximum Likelihood Estimation (MLE) and Expectation-Maximization (EM) Creation. It aims to equips the conventional EKF with ability of approximating noise statistic and its covariance matrices recursively. Moreover, EKF was modified and improved to tune the estimated values given by MLE and EM creation. Besides that, the recursive noise statistic estimators were also estimated based on the unbiased estimation. Although it results high quality solution but it is followed with some risks of non-positive definite matrices of the process and measurement noise statistic covariances. Thus, an addition of Innovation Covariance Estimation (ICE) was also utilized to depress this possibilities. The proposed method is applied for solving SLAM problem of autonomous wheeled mobile robot. Henceforth, it is termed as AEKF-SLAM Algorithm. In order to validate the effectiveness of proposed method, some different SLAM-Based algorithm were compared and analyzed. The different simulation has been showing that the proposed method has better stability and accuracy compared to the conventional filter in term of Root Mean Square Error (RMSE) of Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).

Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots

This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.

Trajectory Tracking of Autonomous Mobile Robots Using Nonlinear Dynamic Inversion

By directly dealing with nonlinear model of the robot this paper presents dynamic inversion as a method of control for reference trajectory tracking of autonomous wheeled mobile robots. Dynamic Inversion control is a technique that guarantees asymptotic stability of the error dynamics leading to appreciable tracking of reference compared to linearised design approach. Nonlinear kinematic model of a differential drive wheeled mobile robot (WMR) is considered as plant. Experimentation has been done to show the implementability of the method in real-time on a real robot. Results are shown for tracking circle and ∞-shaped reference trajectories.

Path planning of a group of robots with potential field approach: decentralized architecture

This paper deals with the path planning problem of a group of autonomous Wheeled Mobile Robots in a very dynamic workspace. The idea consists of considering the system of group of robots as a robot network with decentralized architecture. Each robot plans its trajectory according to its actual position, the position of the other neighbor robots, the position of the obstacles and the position of its target point. So each robot should interact with the other robots in the network to cooperate together in order to plan each robot trajectory. The path planning for every robot is planned based on the potential field approach. The network reacts with the changes of workspace in real time by updating the system equations associated for each robot. The main objective of this work is to avoid collision between robots and obstacles in order to ensure the safety of robots. The solution is tested and simulated with Matlab/Simulink and Solidworks/Simmechanics.

Intelligent Guidance Commands Technique Between two Autonomous Wheeled Mobile Robots

Intelligent Guidance Commands Technique Between two Autonomous Wheeled Mobile Robots

Autonomous Wheeled Mobile Robot Control

The autonomous wheeled mobile robots are very interesting subject both in scientific research and practical applications. The paper deals with the fuzzy control of autonomous wheeled mobile robotic platform motion in an unstructured environment with obstacles. The simulation results show the effectiveness and the validity of the obstacle avoidance behaviour in unstructured environments and the velocity control of a wheeled mobile robotic platform motion of the proposed fuzzy control strategy.

Implementation of a Mobile Robot Platform Navigating in Dynamic Environment

Currently, problems of autonomous wheeled mobile robots in unknown environments are great challenge. Obstacle avoidance and path planning are the back bone of autonomous control as it makes robot able to reach its destination without collision. Dodging obstacles in dynamic and uncertain environment is the most complex part of obstacle avoidance and path planning tasks. This work deals with the implementation of an easy approach of static and dynamic obstacles avoidance. The robot starts by executing a free optimal path loaded into its controller; then, it uses its sensors to avoid the unexpected obstacles which may occur in that path during navigation.

Mobile Robot Obstacle Avoidance Algorithms Based on Information Fusion of Vision and Sonar

In view of the problem of AS-R autonomous wheeled mobile robot obstacle avoidance, a rapid convergence of sonar and binocular stereovision sensor distance information in order to detect and avoid obstacle algorithm is proposed in this paper. The algorithm first uses binocular camera (CCD) to get three-dimensional image of the real environment, through the stereo matching and V-disparity method which is used to calculate disparity map, then obstacles is extracted by Hough lines detection algorithm, finally we will get information about obstacles and sonar return information with T-S fuzzy neural network fusion, then it will output walking controlled decisions. Experimental results proved that the algorithm is effective and practical.