Differential-drive Mobile Robots Research Articles

Second-Order Terminal Sliding Mode Control for Trajectory Tracking of a Differential Drive Robot

This paper proposes a second-order terminal sliding mode (2TSM) approach to the trajectory tracking of the differential drive mobile robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the innermost loop, tracks the robot’s velocity quantities while a kinematic controller, at the outermost loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated, and finite-time convergence is guaranteed by the second-order TSM manifold, which involves higher-order derivatives of the state variables, resulting in an inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

A robust interpolated model predictive control based on recurrent neural networks for a nonholonomic differential-drive mobile robot with quasi-LPV representation: computational complexity and conservatism

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction.

Design and analysis of a mobile robot with novel caster mechanism for high step-overcoming capability

The mobile robot market is experiencing rapid growth, playing a pivotal role in various human-centric environments like restaurants, offices, hotels, hospitals, apartments, and factories. However, current differential-driven mobile robots, employing conventional casters and wheel motors, encounter limitations in surmounting uneven surfaces and high steps due to constraints caused by wheel and caster dimensions. While some robots address these challenges by incorporating optimized wheel shapes and additional motors, this invariably leads to an increase in both size and cost. This research introduces an innovative solution; a novel caster-wheel mechanism designed to enhance the high-step overcoming capability of mobile robots without necessitating alterations to their overall size and structure. By incorporating a sub-wheel linked to a passive joint, the driving force is effeciently converted into a vertical force, thereby empowering the mobile robot to navigate obstacles 85% larger than its caster-wheel radius. Crucially, this innovative caster can be seamlessly manufactured and integrated, offering the potential for widespread adoption as a replacement for conventional casters. Validation through comprehensive simulations and experiments conducted on a prototype robot has been presented in this article, demonstrating its effectiveness even at a robot velocity of 0.1 m/s. This pioneering solution holds significant promise for diverse applications across various mobile robot configurations, presenting a compelling avenue for further exploration and implementation in the field.

AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot.

Embedded model control of networked control systems: An experimental robotic application

In Networked Control System (NCS), the absence of physical communication links in the loop leads to relevant issues, such as measurement delays and asynchronous execution of the control commands. In general, these issues may significantly compromise the performance of the NCS, possibly causing unstable behaviours. This paper presents an original approach to the design of a complete digital control unit for a system characterized by a varying sampling time and asynchronous command execution. The approach is based on the Embedded Model Control (EMC) methodology, whose key feature is the estimation of the disturbances, errors and nonlinearities affecting the plant to control and their online cancellation. In this way, measurement delays and execution asynchronicity are treated as errors and rejected up to a given frequency by the EMC unit. The effectiveness of the proposed approach is demonstrated in a real-world case-study, where the NCS consists of a differential-drive mobile robot (the plant) and a control unit, and the two subsystems communicate through the web without physical connection links. After a preliminary verification using a high-fidelity numerical simulator, the designed controller is validated in several experimental tests, carried out on a real-time embedded system incorporated in the robotic platform.

DESIGN NEURAL NETWORK-PID CONTROLLER FOR TRAJECTORY TRACKING OF DIFFERENTIAL DRIVE MOBILE ROBOT

This paper proposes the design of a neural network controller based on a sample controller for controlling the trajectory-tracking motion of a differential drive mobile robot (DDMR). Firstly, the trajectory tracking model for DDMR is established based on position error. Next, a perceptron neural network is designed with three hidden layers to control the trajectory tracking of DDMR. The backpropagation algorithm is used to train the neural network with training data obtained from the PID controller with time-varying parameters. The authors have developed this approach and experimentally verified it with minor tracking errors. The neural network's weight matrix (W) and bias vector (b) are updated in real-time, providing an advantage over other methods. The effectiveness of the proposed controller is demonstrated by the DDMR's NURBS trajectory tracking error, which does not exceed 2.17 cm, and the DDMR's motion error, with linear and angular velocities not exceeding 0.004 m/s and 0.0007 rad/s, respectively. The proposed controller can supplement traditional controllers in controlling the trajectory of autonomous mobile robots, thereby improving the ability to generate local trajectories to avoid dynamic obstacles by the neural network

Real-Time Digital Simulator Design for Differential Drive Mobile Robot using FPGA

This paper presents a real-time simulation of a differential drive mobile robot (DDMR). The permanent magnet DC motors that drive the robot’s left and right wheels were modeled and executed in real-time on a Field Programmable Gate Array (FPGA) based co-simulator platform, interfacing with the Webots robot simulator, which simulates the DDMR on the PC side. The electrical parameters, which are not available in robot simulators, were simulated and measured by the proposed co-simulator system in real-time under various environmental conditions and trajectories of the robot. Parameters such as current, voltage, and torque were measured instantaneously, enabling a more realistic simulation. Additionally, the cycle time of the robot simulator was determined to be 32 ms, and the developed FPGA-based simulation operated at approximately 2000 times the speed of the robot simulator. The results demonstrate the applicability of the developed platform in robotic applications.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior.

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

AbstractIn this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.

Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage

This paper addresses a systematic method for odometry calibration of a differential-drive mobile robot moving on arbitrary paths in the presence of slippage and an algorithm encoding it which is well fit for online applications. It exploits the redundancy of sensors commonly available on ground mobile robots, such as encoders, gyroscopes, and IMU, to promptly detect slippage phenomena during the calibration process and effectively address their impact on odometry. The proposed technique has been validated through exhaustive numerical simulations and compared with other available odometry calibration methods. The simulation results confirm that the proposed methodology mitigates the impact of poor calibration, conducted without considering possible slipping phenomena, on reaching a target position, reducing the error by up to a maximum of 35 times. This restores the robot’s performance to a calibration condition close to that of a slip-free scenario, confirming the effectiveness of the approach and its robustness against slippage phenomena.

Cooperative object transportation with differential-drive mobile robots: Control and experimentation

Non-prehensile cooperative object transportation is a challenging model problem for distributed control and organization methods but also has practical applications. Therefore, it is widely studied in distributed robotics research. This paper describes and evaluates a novel transportation scheme for differential-drive mobile robots that is, to the authors’ best knowledge, the most versatile scheme of its kind successfully evaluated with real-world hardware. The proposed scheme can conceptually deal with any number of robots and arbitrary polygonal objects, including non-convex ones, without having to retune, retrain, or reconfigure any of the control parameters between different scenarios. This is achieved by splitting the task into a formation control and a formation finding task, both of which are tackled with model-based approaches using distributed optimization. Formation control and formation finding are complicated by the robots’ non-holonomic kinematic constraints. Therefore, a tailored distributed model predictive controller is used for formation control. Finding formations relies on a multibody-dynamics representation of the robots-object system to properly account for contact and non-holonomic constraints. Due to these measures, the transportation scheme achieves a very satisfactory performance and dexterity in real-world hardware experiments utilizing network communication and distributed computation.

Differential-drive Mobile Robot Controller with ROS 2 Support

Los robots conocidos con el nombre de AMR (Autonomous Mobile Robots) se utilizan en la logística interna en muchos tipos de industrias y sectores de la producción. Este tipo de robots sustituyen a los tradicionales AGVs (Automated Guided Vehicles) en los cuales el camino a seguir está definido previamente y no tienen la capacidad de elegir un camino diferente. Por otro lado, los AMRs resultan más flexibles, seguros y precisos, debido a la incorporación de tecnologías que hasta hace poco estaban reservadas al ámbito de la investigación, tales como: navegación autónoma, sistemas de visión por computadoras, tecnología de SLAM (Simultaneous Localization and Mapping), entre otras. Muchas de estas tecnologías se implementan utilizando ROS (Robot Operating System). ROS es un conjunto de bibliotecas de software y herramientas de código abierto y libre para el desarrollo de aplicaciones de robots, cuya nueva versión ROS 2 tiene como uno de sus objetivos ser aplicable a entornos de producción. El presente trabajo describe el desarrollo de un controlador para robots de tracción diferencial tipo AMR con soporte para ROS 2 utilizando la implementación para sistemas embebidos micro-ROS. Este controlador es la evolución de una versión anterior utilizada en diferentes robots por más de 10 años en el CIII (UTN). Vale aclarar que este trabajo está enfocado principalmente en el desarrollo de hardware. Sin embargo, se han realizado algunas pruebas preliminares de software, principalmente, para evaluar el correcto funcionamiento del controlador de tracción diferencial. En primer lugar, se definen los requerimientos de diseño y se selecciona un microcontrolador con soporte nativo para micro-ROS. Luego se describe el desarrollo de cada etapa del controlador, tales como: la alimentación, la comunicación USB, el sensado de tensión de batería, el puerto de depuración y el diseño final del PCB. Por último, se hace mención a las pruebas iniciales de software que permiten verificar el correcto funcionamiento del controlador y las mejoras respecto a la versión anterior.

Concurrent Learning-Based Neuroadaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behaviour of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.

Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm

The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to global path planning, this study proposes an improved bidirectional search Gaussian-A* (BSGA*) algorithm. First, the Gaussian function is introduced to realize the dynamic weighting of the heuristic function, which reduces the calculation time. Secondly, the bidirectional search (BS) structure is adopted to solve the problem of nodes’ repeated search when there are large obstacles between the starting point and the target point. Finally, a multi-layer turning point filter strategy is proposed to further smooth the path. In order to verify the performance of the improved BSGA* algorithm, experiments are carried out in simulation environments with the size of 15 × 15 and 30 × 30, respectively, and compared with the five common global path planning algorithms including ant colony optimization (ACO), D* lite algorithm, and genetic algorithm (GA). The results show that the improved BSGA* algorithm has the lowest calculation time and generates the shortest and smoothest path in the same environment. Finally, the program of the improved BSGA* algorithm is embedded into the LEO ROS mobile robot and two different real environments were built for experimental verification. By comparing with the A* algorithm, Dijkstra algorithm, ACO, D* lite algorithm, and GA, the results show that the improved BSGA* algorithm not only outperforms the above five algorithms in terms of calculation time, length, and total turning angle of the generated paths, but also consumes the least time when DDMR drives along the generated paths.

Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments.

Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot's orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot.

A set-based approach for detecting faults of a remotely controlled robotic vehicle during a trajectory tracking maneuver

This paper addresses the problem of fault detection for a remotely controlled Differential Drive Mobile Robot (DDMR) during trajectory tracking maneuvers. The proposed solution involves two steps. The first step, performed offline, consists of generating a feasible trajectory for the DDMR subject to unknown but bounded external disturbances. To this end, the dynamics of the closed-loop trajectory tracking error are first described using an uncertain system with norm-bounded uncertainty. The optimal feasible trajectory is obtained by a recursive algorithm that involves solving some SDP optimization problems with LMIs constraints. In the second step, which is performed online, the feasibility property of the computed trajectory is exploited to detect faults during the tracking maneuvers. In order to validate the proposed approach, an extensive experimental test campaign was conducted using the Jaguar V4 Dr.Robot platform.

Online Calibration Based on Laser-Encoder Odometry

This paper proposes a new online calibration method for the differential-drive mobile robot equipped with a laser scanner. Our algorithm can jointly estimate the 3-DoF extrinsic parameters of the laser scanner and the intrinsic parameters (radii and wheel spacing) of the differential-drive kinematic model. Applying the pre-integration theory initially developed the for IMU sensor to the differential-drive kinematic model, we adopt iterative nonlinear optimization to minimize the cost derived from laser and encoder measurements. Experiments results confirm that the proposed method can do online calibration precisely.