Differential-drive Mobile Robots Research Articles

Analyzing Behavior of Differential Drive Mobile Robot by Using Statistical Tools

Now a day's robotics provides better service to people anywhere in the world in every sector of life. This standard does not come in a day. Day by day the research on 'analyzing behavior' on robotics by roboticists, engineers, physicists, and mathematician made this standard of life. This paper is presented an analytical approach to comparing the performance of mobile robot. A scenario has been considered in which robot is in world coordinate frame along with a definite boundary. The test run has been taken by varying the microcontrollers and microcomputer. Then analyzed the data’s by graphical and numerical methods that predict a slope which confirms that more the advanced device has been used the robot reached the goal earlier.

Extended state observer-based adaptive sliding mode control of differential-driving mobile robot with uncertainties

Based on the uncertain nonlinear kinematic model of the differential-driving mobile robots, an adaptive sliding mode control method is used to design a controller for trajectory tracking of the differential-driving mobile robots with unknown parameter variations and external disturbances. The total uncertainties of the robot are estimated online by an improved linear extended state observer (ESO) with the error compensating term. The adaptive sliding mode controller with the switching gain is adjustable real-time online is developed by selecting the appropriate PID-type sliding surface. The convergence of the tracking errors for wheeled mobile robots is proved by the Lyapunov stability theory. Moreover, the simulation and real experiment results all show that the effectiveness and superiority of the proposed the adaptive sliding mode control method, in comparison with the traditional sliding model control and backstepping control method.

Fault detection for service mobile robots using model-based method

Detection of faults is a topic of high importance because it increases robot dependability, a requirement for the wide acceptance of service robots in domestic environments. This work takes a model-based approach for detecting and identifying actuator faults on differential-drive mobile robots in an indoor environment. An error-bound is calculated between the estimated and measured robot states which is constantly adapted based on the current state and input signals. A fault is detected when the estimation error is outside this bound. The model parameters are learned by the robot using an adaptive law, after the robot deployment in the target environment. Model uncertainties have an important impact on the fault detection performance, and are dealt with by considering the uncertainty bounds in the bound calculations. This ensures no false alarms occur when the uncertainty remains bounded during normal operation. Furthermore an extension to the method is proposed that addresses the problem of detecting small faults. The method is experimentally validated on a iRobot Roomba autonomous robot.

Duality-Based Nonlinear Quadratic Control: Application to Mobile Robot Trajectory-Following

This paper presents noniterative linearization-based controllers for nonlinear unconstrained systems, coined as extended Rauch–Tung–Striebel (ERTS) and unscented Rauch–Tung–Striebel (URTS) controllers, derived from the duality between optimal control and estimation. The proposed controllers use a Rauch–Tung–Striebel forward–backward smoother as an state estimator to compute the original optimal control problem. The new controllers are applied to trajectory-following problems of differential-drive mobile robots and compared with iterative linear quadratic regulator controller, nonlinear model predictive control, and approximate inference approaches. Simulations show that ERTS and URTS controllers produce almost optimal solutions with a significantly lower computing time, avoiding initialization issues in the other algorithms (in fact, they can be used to initialize them). This paper validates ERTS controller with an experiment of a Pioneer 3-DX mobile robot.

A Novel Control-Navigation System- Based Adaptive Optimal Controller & EKF Localization of DDMR

This paper presents a newly developed approach for Differential Drive Mobile Robot (DDMR). The main goal is to provide a high dynamic system response in the joint space level, the low level control, as well as to enhance the DDMR localization. The proposed approach depends on a Linear Quadratic Regulator (LQR) for the low level control and an Adaptive LQR for the high level control. The investigated DDMR is considered highly nonlinear system due to uncertainty exhibited by the mobile robot incorporated with actuators nonlinearity. DDMR’s uncertainty leads to erroneous localization. An Extended Kalman Filter (EKF) -based approach with fusion sensors is used to enhance the robot degree of belief for its posture. Intensive simulation results obtained from the developed uncertain model and the proposed approach have shown very good dynamic performance on the low level control and very good convergence to the desired posture of the mobile robot path with the presence of robot uncertainty.

Motion Model of Two-Wheel Differential Drive Mobile Robot under Variable Load

Based on kinematics characteristic of two-wheeled differential drive mobile robot (WMR) and response characteristic of fact motor drive system, this paper presents the analysis method of the equivalent rotation inertia, and the entire vehicle load is assigned to each wheel, and then the wheel load is converted into the corresponding equivalent rotation inertia of the motor shaft of each wheel, and motion model of WMR are obtained for combining with quasi-equivalent (QE) state space model of double-loop direct current motor systems under variable load and kinematics model of WMR under the load changes. By using speed response data of the actual system and combining with genetic algorithm to accurately identify the model parameters. Finally, through experiments results of the WMR motion model and the second order model respectively comparing with the actual system which demonstrates the effectiveness of the proposing method and model.

Tracking Control for Differential-Drive Mobile Robots With Diamond-Shaped Input Constraints

This brief is concerned with the tracking control of differential-drive mobile robots with diamond-shaped input constraints. The strategy of designing time-varying feedback parameters for the tracking controller is used to cope with the challenge presented by input constraints. Sufficient conditions on the feedback parameters are proposed to guarantee the asymptotic stability of the tracking error system in the absence of input constraints. The feedback parameters are designed by applying a geometric analysis approach, so that the sufficient conditions on feedback parameters and the input constraints are satisfied. Experimental results are included to validate the effectiveness of the proposed controller.

Waypoints guidance of differential-drive mobile robots with kinematic and precision constraints

SUMMARYThis paper proposes a new kinematic controller for the waypoints guidance of robotic mobile platforms. A notable feature of the controller is its ability to process the raw sequence of waypoints to produce smooth reference velocities from control laws that are derived by taking into account adriving profileincluding the velocity limits, the acceleration limits, the motion modes through each waypoint (forward or backward) and the precision constraints that are required to ensure accurate waypoints traversal. A mathematical analysis demonstrates the convergence of the movements through the waypoints sequence. In addition, we present a simple way to adapt the driving profile in order that the platform reaches the last waypoint at a prescribed time. A feed-forward unit is finally described, that compensates for delays and first-order poles in the velocity response of the platform. Various simulations and experiments on real robotic platforms demonstrate the behavior and the effectiveness of the solution.

Input‐constrained formation control of differential‐drive mobile robots: geometric analysis and optimisation

This study considers the formation control of differential-drive mobile robots subject to diamond-shaped input constraints. A basic controller form in terms of two feedback functions is proposed in a leader-follower setup. The existence of suitable feedback functions, guaranteeing the achievement of desired formation and the satisfaction of input constraints, is confirmed by a sufficient condition derived from geometric analysis. The geometric analysis also helps selections of feedback functions. Optimal feedback functions are designed to fully exploit the maneuverability of mobile robots. In addition, the performance assignment, which confines the velocities of the follower robot in a specified subset of diamond-shaped input domain to avoid slippage and reduce mechanical wear, is discussed. Simulation results are included to verify the effectiveness of the proposed controllers.

RFID-Based Mobile Robot Trajectory Tracking and Point Stabilization Through On-line Neighboring Optimal Control

In this manuscript, we propose an on-line trajectory-tracking algorithm for nonholonomic Differential-Drive Mobile Robots (DDMRs) in the presence of possibly large parametric and measurement uncertainties. Most mobile robot tracking techniques that depend on reference RF beacons rely on approximating line-of-sight (LOS) distances between these beacons and the robot. The approximation of LOS is mostly performed using Received Signal Strength (RSS) measurements of signals propagating between the robot and RF beacons. However, an accurate mapping between RSS measurements and LOS distance remains a significant challenge and is almost impossible to achieve in an indoor reverberant environment. This paper contributes to the development of a neighboring optimal control strategy where the two major control tasks, trajectory tracking and point stabilization, are solved and treated as a unified manner using RSS measurements emitted from Radio Frequency IDentification (RFID) tags. The proposed control scheme is divided into two cascaded phases. The first phase provides the robot's nominal control inputs (speeds) and its trajectory using full-state feedback. In the second phase, we design the neighboring optimal controller, where RSS measurements are used to better estimate the robot's pose by employing an optimal filter. Simulation and experimental results are presented to demonstrate the performance of the proposed optimal feedback controller for solving the stabilization and trajectory tracking problems using a DDMR.

Simultaneous Calibration of Odometry and Sensor Parameters for Mobile Robots

Consider a differential-drive mobile robot equipped with an on-board exteroceptive sensor that can estimate its own motion, e.g., a range-finder. Calibration of this robot involves estimating six parameters: three for the odometry (radii and distance between the wheels) and three for the pose of the sensor with respect to the robot. After analyzing the observability of this problem, this paper describes a method for calibrating all parameters at the same time, without the need for external sensors or devices, using only the measurement of the wheel velocities and the data from the exteroceptive sensor. The method does not require the robot to move along particular trajectories. Simultaneous calibration is formulated as a maximum-likelihood problem and the solution is found in a closed form. Experimental results show that the accuracy of the proposed calibration method is very close to the attainable limit given by the Cramer–Rao bound.

Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework

This paper presents a unified dynamic modeling framework for differential-drive mobile robots (DDMR). Two formulations for mobile robot dynamics are developed; one is based on Lagrangian mechanics, and the other on Newton-Euler mechanics. Major difficulties experienced when modeling non-holonomic systems in both methods are illustrated and design procedures are outlined. It is shown that the two formulations are mathematically equivalent providing a check on their consistency. The presented work leads to an improved understanding of differentialdrive mobile robot dynamics, which will assist engineering students and researchers in the modeling and design of suitable controllers for DDMR navigation and trajectory tracking.

Hybrid Potential Field Based Control of Differential Drive Mobile Robots

This paper suggests a new way for nonholonomic mobile robots to navigate in obstacle environments using potential fields based on navigation functions. The proposed strategy is a time-invariant feedback control design with the distinguishing feature that it requires almost no switching compared to alternative methodologies of the same nature. Asymptotic convergence with collision avoidance for the proposed approach is established analytically, and the method is demonstrated on a differential-drive skid steering mobile robot.

A Differential-Drive Mobile Robot Driven by an Ethology Inspired Behaviour Architecture

This paper presents the work in progress of an ethology inspired action selection mechanism to control a differentialdrive mobile robot with potential applications in the fields of security, defense, reconnaissance, and others. The mathematical model of a two wheel differential-drive model is presented. The model shows how zero turning radius is achieved with only bidirectional movement. The mobile robot is driven by behaviour patterns of the behavioural architecture that are used to map the incoming stimuli from ultrasound sensors into responses that affect the voltage's intensity of each wheel's motor. Therefore, it controls the translational and rotational movements of the mobile robot described herewith.

A Method for Modeling and Simulation of Differential Drive Mobile Robot Localization Based on EKF Multisensor Fusion

A method was proposed based on principle of EKF (extended Kalman Filter) state estimation to improve the localization precision of differential drive mobile robot. The robot position was estimated by Multisensor fusion of Odometer and laser, which kinematics model, the robot sensor perception model and the sensor error model were presented. The models were introduced into the state estimation and covariance matrix update equation of EKF with match convergence condition and nonlinear error correction covariance matrix.The specific iteration equation was acquired.The simulation results demonstrate the approach of modeling and fusion is accurate and validity.

Design of switching path-planning control for obstacle avoidance of mobile robot

Generally speaking, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, and planning a real-time trajectory to reach the object. In this process, the issue of obstacle avoidance is a fundamental topic to be challenged. Thus, a switching path-planning control scheme is designed without detailed environmental information, large memory size, and heavy computation burden in this study for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection designed by learning and expert rules for enhancing the tracking speed and adapting to different environments. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations and experimental results of a differential-driving mobile robot under the possible occurrence of obstacle shapes.

Localization of a high-speed mobile robot using global features

SUMMARYA new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors, since the distance to the beacon is measured based on the traveling time of the ultrasonic signal. When the mobile robot is moving slowly, the measurement time does not yield a high error. At a higher speed, however, the localization error becomes too large for the mobile robot to be located accurately. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons, only one active beacon and the global features of the mobile robot are used to localize the mobile robot. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot, which generally describe the motion of, and are used for the trace prediction of, a mobile robot. In high-speed operations, the localizer finds the intersection point of this predicted trace and the circle, which is centered at the beacon whose radius is the distance between the mobile robot and the beacon. This new approach overcomes the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm is verified through experiments with a high-speed mobile robot.

A non-iterative and effective procedure for simultaneous odometry and camera calibration for a differential drive mobile robot based on the singular value decomposition

Differential-drive mobile robots are usually equipped with video cameras for navigation purposes. In order to ensure proper operational capabilities of such systems, several calibration steps are required to estimate the video-camera intrinsic and extrinsic parameters, the relative pose between the camera and the vehicle frame and the odometric parameters of the vehicle. In this paper, simultaneous estimation of the aforementioned quantities is achieved by a novel and effective calibration procedure. The proposed calibration procedure needs only a proper set of landmarks, on-board measurements given by the wheels encoders, and the camera (i.e., a number of properly taken camera snapshots of the set of landmarks). A major advantage of the proposed technique is that the robot is not required to follow a specific path: the vehicle is asked to roughly move around the landmarks and acquire at least three snapshots at some approximatively known configurations. Moreover, since the whole calibration procedure does not use external measurement devices, it can be used to calibrate, on-site, a team of mobile robots with respect to the same inertial frame, given by the position of the landmarks’ tool. Finally, the proposed algorithm is systematic and does not require any iterative step. Numerical simulations and experimental results, obtained by using a mobile robot Khepera III equipped with a low-cost camera, confirm the effectiveness of the proposed technique.

Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network

This study focuses on the design of robust path tracking control for a mobile robot via a dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.

Fuzzy Logic Controller for Bidirectional Garaging of a Differential Drive Mobile Robot

This paper presents a procedure for the design of a fuzzy logic controller for the garaging of a wheeled mobile robot, with non-holonomic constraints and discrete control signal levels. The design procedure is based on the virtual fuzzy magnet principle, which implies the definition of fictive points in the garage surroundings, and which, in an appropriate manner, by robot attraction, provides an efficient garaging process. The proposed fuzzy logic controller has four input variables: two represent the robot's distancing from fictitious fuzzy magnets and two are angles that define the orientation of the vehicle. The output variables are related to voltages sent to motors in charge of propelling the left and right wheel of the robot. The efficiency and shortfalls of the proposed algorithm are analyzed by means of both detailed simulations and multiple re-runs of a real experiment. Special attention is devoted to the analysis of different initial robot configurations and the effect of an error in the estimation of the current position of the robot on garaging efficiency.