Control Of Mobile Robot Research Articles

Recent developments and applications of simultaneous localization and mapping in agriculture

AbstractSimultaneous Localization and Mapping (SLAM) is a process to use multiple sensors to position an unmanned mobile vehicle without previous knowledge of the environment, and meanwhile construct a map of this environment for the further applications. Over the past three decades, SLAM has been intensively researched and widely applied in mobile robot control and unmanned vehicle navigation. SLAM technology has demonstrated a great potential in autonomously navigating the mobile robot and simultaneously reconstructing the three‐dimensional (3D) information of surrounding environment. With the vigorous driving of sensor technology and 3D reconstruction algorithms, many attempts have been conducted to propose novel systems and algorithms combined with different sensors to solve the SLAM problem. Notably, SLAM has been extended to various aspects of agriculture involved with autonomous navigation, 3D mapping, field monitoring, and intelligent spraying. This paper focuses on the recent developments and applications of SLAM, particularly in complex and unstructured agricultural environment. A detailed summary of the developments of SLAM is given from three main fundamental types: light detection and ranging SLAM, Visual SLAM, and Sensor Fusion SLAM, and we also discuss the applications and prospects of SLAM technology in agricultural mapping, agricultural navigation, and precise automatic agriculture. Particular attention has been paid to the SLAM sensors, systems, and algorithms applied in agricultural tasks. Additionally, the challenges and future trends of SLAM are reported.

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy.

Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a nonparametric regression method. The velocity uncertainties are estimated online using the real-time measured data. The prediction mean and variance of the GPR are used to counteract the effect of the uncertainties and to design the robust control term, respectively. A continuous robust controller is proposed which has the advantage of achieving asymptotic convergence of the trajectory tracking error instead of uniformly ultimately boundedness. Comparisons with the neural network based adaptive controller and a state of the art GPR-based design demonstrate the effectiveness of the proposed control strategy.

A Smooth Time–Varying PID Controller for Nonholonomic Mobile Robots Subject to Matched Disturbances

A Smooth Time–Varying PID Controller for Nonholonomic Mobile Robots Subject to Matched Disturbances

Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots Using Model Predictive Control Subjected to Lyapunov-based Input Constraints

Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots Using Model Predictive Control Subjected to Lyapunov-based Input Constraints

Clonal selection algorithm based control for two-wheeled self-balancing mobile robot

In this study, we propose adaptive control approaches based on optimization for a two-wheeled mobile robot. The mathematical model of the self-balancing vehicle based on the inverted pendulum principle, is derived from the Lagrange method and the vehicle model is simulated within MATLAB/Simulink environment. Five different optimization-based controller models (Model 1–5) are developed based on common control methods, which are the Pole Placement Design Method, PID, and LQR. Besides the stability control of the robot, speed control and trajectory tracking control are also provided with these models. While improving the optimization-based controller models, the CSA, an artificial immune optimization methods, is adapted for use with the system. The benefits of the study are given as follows: 1) The balancing control and trajectory tracking control performance of the two-wheeled vehicle is improved using the proposed optimization-based control approach; 2) A comprehensive study is introduced with different control models based on Pole Placement Design Method, PID, and LQR Method; 3) Since parameter tuning of multiple PID controllers is challenging for a two-wheeled vehicle constructed by using the inverted pendulum principle, one of the proposed control models is developed using multiple PIDs. To alleviate the difficulties of parameter tuning for multiple PIDs, the CSA method is adapted for a self-balancing two-wheeled vehicle for the first time; 4) To exhibit the optimization effect of the CSA, the simulation results are compared with five other optimization methods, namely PSO, ABC, DE, GoldSa-II, and CS; 5) The effectiveness of the proposed optimization-based control approaches and optimization method are demonstrated through analyses and simulation studies. In this concept, it has been also analyzed effects of the external disturbances and uncertain parameters on the proposed control method.

Research on Self-balancing Two Wheels Mobile Robot Control System Analysis

The paper presents the research on self-balancing two-wheels mobile robot control system analysis with experimental studies. The research problem in this work is to stabilize the mobile robot with self-control and to carry the sensitive things without failing in a long span period. The main objective of this study is to focus on the mathematical modelling of mobile robot from laboratory scale to real world applications. The numerical expression with mathematical modelling is very important to control the mobile robot system with linearization. The fundamental concepts of dynamic system stability were utilized for maintaining the stability of the constructed mobile robot system. The controller design is also important for checking the stability and the appropriate controller design is proportional, integral,and derivative – PID controller and Linear Quadratic Regulator (LQR). The steady state error could be reduced by using such kind of PID controller.The simulation of numerical expression on mathematical modeling was conducted in MATLAB environments. The confirmation results from the simulation techniques were applied to construct the hardware design of mobile robot system for practical study. The results from simulation approaches and experimental approaches are matched in various kinds of analyses. The constructed mobile robot system was designed and analyzed in the control system design laboratory of Yangon Technological University (YTU).

Distributed leader-following formation control for multiple nonholonomic mobile robots via bioinspired neurodynamic approach

This paper investigates the distributed leader-following formation control problem for multiple nonholonomic wheeled mobile robots via a bioinspired neurodynamic approach. To do this, first, we develop a distributed estimator for each follower robot which uses its own information and the information of its neighboring robots to estimate the leader’s states. Second, a formation tracking control law is proposed for each follower robot based on the estimated states of the leader using the backstepping technique. Then, a bioinspired neurodynamics-based backstepping controller is designed to solve the impractical velocity jumps problem. Furthermore, Lyapunov function-based approach is utilized to derive sufficient conditions which guarantee asymptotic stability of the multiple mobile robot system. Finally, simulation results are presented to illustrate the effectiveness of the proposed controllers.

THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

Path Tracking Control of Skid-steered Mobile Robot on the Slope Based on Fuzzy System and Model Predictive Control

Path Tracking Control of Skid-steered Mobile Robot on the Slope Based on Fuzzy System and Model Predictive Control

Coverage Control of Mobile Robots With Different Maximum Speeds for Time-Sensitive Applications

This letter presents a coverage control algorithm for robots with different maximum speeds to effectively cover an area in terms of time of travel. In order to utilize the different speeds of the robots, the region of dominance of a robot is approximated by the set of points where the robot can arrive faster than any other robots in the team by cruising at its maximum speed. Multiplicatively weighted Voronoi diagrams are used to express these speed weighted regions of dominance of the robots. The new cost function of the system takes time instead of distance as its basis, and it is interpreted as the summation of time for robots to reach all weighted points in the coverage domain. We use a controller that drives the robots towards the weighted centroids of their cells, which locally minimizes the new cost function. The resulting final configuration of the robots is shown to provide a temporally optimal coverage instead of a spatially optimal coverage over a domain.

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecanum-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.

Global path planning based on a bidirectional alternating search A* algorithm for mobile robots

While the A* algorithm has been widely investigated and applied in path planning problems, it has outstanding issues such as long calculation time, large turning angles, and the unsmoothed path in large task spaces. Aiming at overcoming these problems, an improved A* algorithm is proposed in this work. First, a bidirectional alternating search (BAS) strategy is introduced into the A* algorithm; the forward and backward path lists use the current path node in the opposite list as the target node to alternately search for paths until the paths meet, which improves the search efficiency of the algorithm. Then, the heuristic function is weighted via exponential attenuation, which overcomes the shortcomings of the BAS-A* algorithm. Finally, the filtering function of path nodes is introduced to reduce redundant nodes in the path, thereby effectively reducing the turning angle. Moreover, the use of Bézier curves fulfills the requirements of smooth path planning, which is critical for the motion control of mobile robots. Simulations of path planning on maps of different sizes were performed, and the proposed algorithm was compared with that of the A* algorithm, the genetic algorithm (GA) and the simulated annealing algorithm (SA) in terms of the computation time, path length, and turning angle. The results reveal that the proposed algorithm can solve the robot path planning problem more efficiently and smoothly than the other algorithms mentioned above. Additionally, the practicability of the proposed algorithm is validated on the TurtleBot3 Waffle Pi mobile robot.

Experimental Verification of the Differential Games and H∞ Theory in Tracking Control of a Wheeled Mobile Robot

Experimental Verification of the Differential Games and H∞ Theory in Tracking Control of a Wheeled Mobile Robot

Recognition and Localization of Target Images for Robot Vision Navigation Control

This paper focuses on a visual navigation control system for mobile robots, recognizing target images and intelligent algorithms for the navigation system’s path tracking and localization techniques. This paper examines the recognition and localization of target images based on the visual navigation control of mobile robots. It proposes an efficient marking line method for recognizing and localization target images. Meanwhile, a fuzzy control method with smooth filtering and high efficiency is designed to improve the stability of robot operation, and the feasibility is verified in different scenarios. The corresponding image acquisition system is developed according to the characteristics of the experimental environment, and the acquired images are preprocessed to obtain corrected grayscale images. Then, target image recognition and linear fitting are performed to obtain target image positioning. The system calculates the angle and distance of the mobile robot, offsetting the target image in real time, adjusting the output signal, and controlling the mobile robot to realize path tracking. The comparison of sensor data and path tracking algorithm results during the experiment shows that the path tracking algorithm achieves good results with an angular deviation of ±1.5°. The application of RANSAC algorithm and improved Hough algorithm was analyzed in visual navigation control, and the two navigation line detection algorithms based on the image characteristics of the target image were improved in the optical detection area of the navigation line for the shortcomings of the two algorithms in visual navigation control, and the algorithms before and after the improvement were compared.

Frictional forces and torques compensation based cascaded sliding-mode tracking control for an uncertain omnidirectional mobile robot

The work presented here deals with the trajectory-tracking control of omnidirectional mobile robots using a novel robust control technique. The aim is to achieve good tracking performance in the presence of frictional forces and torques, uncertainties, and saturated control inputs. First, a dynamic model of the omnidirectional mobile robot incorporating motor dynamics and frictional forces and torques is derived leading to a higher order system model. Subsequently, a novel cascaded sliding-mode tracking control scheme is proposed to perform trajectory-tracking with the omnidirectional mobile robot, which includes direct and indirect sliding-mode controllers in a cascaded structure. The indirect sliding-mode controller is utilized to attain tracking of the indirect reference input (i.e., desired trajectory) in the first subsystem and constitutes a direct reference input for the second one. A direct sliding-mode controller (i.e., actual control) is then designed to achieve tracking of the direct reference input. This way, the indirect reference input can be effectively manipulated through the proposed cascaded sliding-mode tracking control and individual subsystems are guaranteed to have not only Uniform Ultimate Boundedness but also asymptotic convergent performance. The stability of the closed-loop system is demonstrated via the Lyapunov stability criteria and validated through simulations. It is shown that the cascaded sliding-mode tracking control leads to good trajectory-tracking performance in the presence of uncertainties, friction, and saturated control input.

Privacy-preserving dynamic average consensus via state decomposition: Case study on multi-robot formation control

Dynamic average consensus is a decentralized control/estimation framework where a group of agents cooperatively track the average of local time-varying reference signals. In this paper, we develop a novel state decomposition-based privacy preservation scheme to protect the privacy of agents when sharing information with neighboring agents. Specifically, we first show that an external eavesdropper can successfully wiretap the reference signals of all agents in a conventional dynamic average consensus algorithm. To protect privacy against the eavesdropper, a state decomposition scheme is developed where the original state of each agent is decomposed into two sub-states: one succeeds the role of the original state in inter-node interactions, while the other sub-state only communicates with the first one and is invisible to other neighboring agents. Rigorous analyses are performed to show that (1) the proposed privacy scheme preserves the convergence of the average consensus; and (2) the privacy of the agents is protected such that an eavesdropper cannot discover the private reference signals with guaranteed accuracy. The developed privacy-preserving dynamic average consensus framework is then applied to the formation control of multiple non-holonomic mobile robots, in which the efficacy of the scheme is demonstrated. Numerical simulation is provided to illustrate the effectiveness of the proposed approach.

Formation control of multiple wheeled mobile robots based on model predictive control

AbstractThis paper considers the problem of formation control for a team of nonholonomic wheeled mobile robots considering obstacle avoidance. A new control algorithm based on the model predictive control (MPC) and the nonlinear dynamics of the system is presented here. The control algorithm is applied to the nonlinear system using two different controllers including linear MPC and nonlinear MPC. The virtual structure formation approach and artificial potential field method are employed to determine the reference trajectories of the robots and to solve the problem of obstacle avoidance. A control algorithm consisting of two parts is proposed to track the trajectories and maintain the team’s formation. Two advantages of using MPC techniques are the ability to consider control and state constraints which are of high importance in practical applications. The main contribution of this paper is the design of two robust control systems to disturbance with respect to actuator saturation limits. Simulation results demonstrate the effectiveness and robustness of the proposed control algorithm in trajectory tracking and formation maintenance in the presence of disturbance and actuator limits.

The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.

Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation.

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.