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In 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. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents the design of a novel trajectory tracking control strategy and the development of a mathematical model for a non-holonomic differential-drive wheeled mobile robot. The proposed control system utilizes a dual-loop approach, where the inner loop controls the dynamics by employing Adaptive Fuzzy Sliding Mode Control (AFSMC), and the outer loop, handles kinematics by utilizing an Adaptive Neuro-Fuzzy Inference System ;(ANFIS). The ANFIS is employed to minimize the error between the actual and desired velocities, providing a desired input for the inner loop. Meanwhile, the AFSMC is used to effectively control the system dynamics. The use of these dual-loop controllers considerably improves the system’s overall efficiency. The inner controller compensates for dynamic disturbances, while the outer controller manages velocity errors. We integrate the actuator dynamics and the chopper effect of the wheels in the dynamics modeling, which helps to increase the models accuracy. MATLAB was used to implement the controller, while circular and eight-shaped trajectories were generated to assess the performance of the proposed controller. In addition, a comparative analysis of different controllers such as PID, SMC, AFSMC, and AFSMC with ANFIS was presented. The simulations were conducted under uncertainties, and the proposed controller is better than other controllers at tracking desired trajectories. The Lyapunov stability analysis is employed to verify the stability of the proposed controller. This paper shows that the proposed dual-loop controller is stable and more robust to internal parameter variation and external disturbance for the examined system. In general, the AFSMC with ANFIS is superior in trajectory tracking for the examined system compared to other controllers.

Mobile robot concept is one of the most commonly used nonholonomic system for industrial and academic autonomous applications. There are many types of mobile robot design concepts and control strategies which have been continuously developed by researchers. In this study, two wheeled differential drive mobile robot (DDMR) is used for trajectory tracking study under different conditions. Reference trajectory, dynamic and kinematic motion models of DDMR are defined as mathematical expressions in computer software. For tracking the reference trajectory, error between current pose and reference pose was decreased by sliding mode controller (SMC) and proportional–integral–derivative controller (PID) with kinematic based backstepping controller (KBBC) respectively. A reference path which consists of sinusoidal and linear parts tracked by both controller combinations in first simulation to examine controller tracking performances. In order to examine and compare; responsiveness, stability and robustness of the controllers, an additional mass which affects motion dynamics of DDMR vertically added to the mobile robot body during trajectory tracking application. All results and discussions are comparatively stated at the end of the study with related error figures and evaluations.

This paper presents the design of a novel trajectory tracking control strategy and the development of a mathematical model for a non-holonomic differential-drive wheeled mobile robot. The proposed control system utilizes a dual-loop approach, where the inner loop controls the dynamics by employing Adaptive Fuzzy Sliding Mode Control (AFSMC), and the outer loop, handles kinematics by utilizing an Adaptive Neuro-Fuzzy Inference System ;(ANFIS). The ANFIS is employed to minimize the error between the actual and desired velocities, providing a desired input for the inner loop. Meanwhile, the AFSMC is used to effectively control the system dynamics. The use of these dual-loop controllers considerably improves the system's overall efficiency. The inner controller compensates for dynamic disturbances, while the outer controller manages velocity errors. We integrate the actuator dynamics and the chopper effect of the wheels in the dynamics modeling, which helps to increase the models accuracy. MATLAB was used to implement the controller, while circular and eight-shaped trajectories were generated to assess the performance of the proposed controller. In addition, a comparative analysis of different controllers such as PID, SMC, AFSMC, and AFSMC with ANFIS was presented. The simulations were conducted under uncertainties, and the proposed controller is better than other controllers at tracking desired trajectories. The Lyapunov stability analysis is employed to verify the stability of the proposed controller. This paper shows that the proposed dual-loop controller is stable and more robust to internal parameter variation and external disturbance for the examined system. In general, the AFSMC with ANFIS is superior in trajectory tracking for the examined system compared to other controllers. [ABSTRACT FROM AUTHOR]

Mobile robot concept is one of the most commonly used nonholonomic system for industrial and academic autonomous applications. There are many types of mobile robot design concepts and control strategies which have been continuously developed by researchers. In this study, two wheeled differential drive mobile robot (DDMR) is used for trajectory tracking study under different conditions. Reference trajectory, dynamic and kinematic motion models of DDMR are defined as mathematical expressions in computer software. For tracking the reference trajectory, error between current pose and reference pose was decreased by sliding mode controller (SMC) and proportional–integral–derivative controller (PID) with kinematic based backstepping controller (KBBC) respectively. A reference path which consists of sinusoidal and linear parts tracked by both controller combinations in first simulation to examine controller tracking performances. In order to examine and compare; responsiveness, stability and robustness of the controllers, an additional mass which affects motion dynamics of DDMR vertically added to the mobile robot body during trajectory tracking application. All results and discussions are comparatively stated at the end of the study with related error figures and evaluations. [ABSTRACT FROM AUTHOR]

Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot's movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better. [ABSTRACT FROM AUTHOR]

This study addresses the problem of efficiently navigating a differential drive mobile robot to a target pose in a region with obstacles, without explicitly generating a trajectory. The robot is assumed to be equipped with an omnidirectional range sensor, while the region may or may not be a priori known. Given the known obstacles in each iteration of the controller, the shortest path connecting the robot and the target point provides a raw desired movement direction. Considering the unobstructed area in that direction, the size of the robot and the obstacle contours in its visibility range, the reference direction is determined. Finally, respecting the velocity and acceleration constraints of the robot, the angular velocity is properly selected to rotate the robot towards the reference direction, while the linear velocity is chosen to efficiently minimise the distance to the final target, as well as to avoid collisions. After the robot has reached the target, the controller switches to orientation mode in order to fix the orientation. Experimental studies demonstrate the effectiveness of the algorithm.

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system.

Gesture recognition technology can be applied in a variety of plans. In this study, a simple hand gesture recognition has been applied to a mobile robot to make it easier for someone to operate. The mobile robot uses differential drive and it is equipped with a Wi-Fi device. The simple hand gesture sensor used in this study is ZX sensor. The sensor outputs are then processed by fuzzy logic control. The use of fuzzy logic in controlling robots makes changes in wheel speed when turning smooth. The response time of the wheel when turning based on testing is around 2-4 seconds with fuzzy logic control and less than 1 second with on-off control. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a method for trajectory planning and control of a mobile robot for application in picture drawing from images. The robot is an accurate differential drive mobile robot platform controlled by a field-programmable-gate-array (FPGA) controller. By not locating the tip of the pen at the middle between two wheels, we are able to construct an omnidirectional mobile platform, thus implementing a simple and effective trajectory control method. The reference trajectories are generated based on line simplification and B-spline approximation of digitized input curves obtained from Canny's edge-detection algorithm on a gray image. Experimental results for image picture drawing show the advantage of this proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of finding a time-optimal velocity profile along the predefined path for static formations of mobile robots in order to traverse the path in shortest time and to satisfy, for each mobile robot in the formation, velocity, acceleration, tip over and wheel slip prevention constraints. Time-optimal velocity planning is achieved using so called bang-bang control where minimum and maximum accelerations of the formation are alternating. The developed trajectory planning algorithm is demonstrated on the formation of differential drive mobile robots. [ABSTRACT FROM AUTHOR]

This paper presents formulation of a novel block-backstepping based control algorithm to overcome the challenges posed by the tracking and the stabilization problem for a differential drive wheeled mobile robot (WMR). At first, a two-dimensional output vector for the WMR has been defined in such a manner that it would decouple the two control inputs and, thereby, allow the designer to formulate the control laws for the two inputs one at a time. Actually, the decoupling has been carried out in a way to convert the system into block-strict feedback form. Thereafter, block-backstepping control algorithm has been utilized to derive the expressions of the control inputs for the WMR system. The proposed block-backstepping technique has further been enriched by incorporating an integral action for enhancing the steady state performance of the overall system. Global asymptotic stability of the overall system has been analyzed using Lyapunov stability criteria. Finally, the proposed control algorithm has been implemented on a laboratory scale differential drive WMR to verify the effectiveness of the proposed control law in real-time environment. Indeed, the proposed design approach is novel in the sense that it has judiciously exploited the nonholonomic constraint of the WMR to result in a reduced order block-backstepping controller for the WMR, and thereby, it has eventually yielded a compact expression of the control law that is amenable to real-time implementation. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Differential drive mobile robots are widely used due to their simplicity, easiness of control and flexibility. This paper discuss a detailed modeling of a differential drive robot taking into account the kinematics, actuator dynamics and rolling resistances of the wheels. Controllers have been designed for smooth trajectory tracking. Different trajectories similar to real life scenarios have been created and the model and control algorithm are seen to give accurate trajectory tracking. [ABSTRACT FROM AUTHOR]

In this paper, we present new Learning from Demonstration-based algorithm that generalizes and extracts relevant features of desired motion trajectories for differential drive mobile robots. The algorithm is tested through series of simulations and real world experiments in which desired task is demonstrated by the human teacher while teleoperating the mobile robot in the working environment. In the first step of the developed method, Gaussian Mixture Model (GMM) is built for incremental motions of the mobile robot between two consecutive poses. After this, the hidden Markov model is used to capture transitions between states (temporal variations of the data between clusters) which are missing from static GMM representation. Generalization of the motion is achieved by using the concept of keyframes, defined as points in which significant changes between GMM/HMM states occur. In the second step, the resulting GMM/HMM representation is used to generate optimal state sequences for each demonstration and to temporally align them, using 1D dynamic time warping, with respect to the one most consistent with the GMM/HMM model. This phase implies extraction of keyframes along all state sequences and projecting them into control space, in which controls are aligned in time as well. Finally, the generalized controls are obtained by averaging over all controls at the keyframes; simple piecewise cubic spline method is used for interpolation between generated control values. The main advantage of the developed algorithm is its ability to learn and generalize from all demonstrated examples which results in high quality reproductions of the motion. The proposed approach is verified both in simulated environment and using real mobile robot. [ABSTRACT FROM AUTHOR]

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system. [ABSTRACT FROM AUTHOR]