Problem Of Mobile Robot Navigation Research Articles

Research on autonomous mobile robot maze navigation problem based on Dijkstras algorithm

In recent years, the field of autonomous mobile robotics has garnered significant attention due to its potential applications in various domains such as logistics, surveillance, and search and rescue operations. A crucial challenge in this area is the efficient navigation of robots within complex and dynamic environments, particularly when navigating through maze-like structures. The maze navigation problem involves finding optimal paths for robots to traverse from their initial positions to designated destinations while avoiding obstacles and making intelligent decisions to ensure timely and safe navigation. This study aims to investigate and apply Dijkstras algorithm to solve the maze navigation problem for autonomous mobile robots. By analyzing the navigation challenges faced by autonomous mobile robots in maze environments, a solution based on Dijkstras algorithm is proposed. In conclusion, this study contributes to the field of autonomous mobile robotics by proposing and evaluating the application of Dijkstras algorithm for maze navigation. The experimental results validate its potential to address the challenges of navigating intricate maze environments. However, it is acknowledged that further refinement and innovation are possible to continue improving the performance of autonomous mobile robots in maze navigation scenarios.

Flexible Path Planning of Mobile Robot for Avoiding the Dynamic Obstacles Using Fuzzy Controllers

Mobile robots are increasingly used in service and industrial activities. For many previous decades, its navigation has always remained an open problem for researchers in the World. The target of this research is to develop a control method for mobile robots operating in dynamic and unknown environments with both static and moving obstacles. The method utilizes a fuzzy logic approach based on a Lidar sensor to gather information about the environment and make decisions for navigation. The study focuses on the use of mobile robots in material handling applications such as warehouses or libraries with flat floors. The results demonstrate the ability of the proposed method to navigate the mobile robot around both static and moving obstacles, enabling its use in various material handling applications. The study provides a potential solution for the open problem of mobile robot navigation in complex environments.

Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model‐based map representation

AbstractLocalization is a core problem in mobile robot navigation. Simultaneous localization and mapping (SLAM) costs much for an unmanned aerial vehicle (UAV). This research aims to design an orthogonal laser scan device for localization and to save computation costs. Based on disturbance analysis, residual influences on sensor state are quantitative, and they are related to uncertainty and sensitivity. This research applied the residual selection method to a UAV. The feature point detection utilises multi‐scale and Gaussian model fitting techniques to guarantee true positives. The map is represented by Gaussian Mixture Models (GMM) with lower memory costs. The orthogonal laser scan device is composed and placed on a UAV for real‐time three‐dimensional localization, whose errors are at the centimeter level.

Autonomous Robot Navigation in Known Environment

Autonomous robot navigation is one of the challenging researched topic in robotics. A secure and optimal path in known environment is required for any mobile robot navigation for navigation purpose. In this work, a Simulink model is proposed based on Pure Pursuit and path following controllers for solving the problem of mobile robot navigation in a known environment is presented. Pure Pursuit controller is used to find the linear and angular velocities of the robot. Moreover, (x, y) coordinate position of robot and waypoints are input to the pure pursuit block. Velocity commands are sent to drive robot on the given path. The main aim of the proposed mod el is to find the obstacle free path for the mobile robot navigation. However, the robot is to navigate from start to target location without hitting obstacles. For experimental results, Turtle Bot Gazebo simulator is used. "Robotic system Toolbox" of the MATLAB is used to program the navigation process.

A Unified Multiple-Motion-Mode Framework for Socially Compliant Navigation in Dense Crowds

Mobile robots are expected to move safely and efficiently in socially-compliant ways in dense crowds. In this paper, a unified multiple-motion-mode framework is proposed to achieve socially-compliant navigation in dense crowds. The proposed framework consists of three successive phases, the identification of pedestrian groups and prediction of their trajectories, the generation of candidate local trajectories of the robot, and the determination of an optimal local trajectory. The pedestrians are grouped according to distances among pedestrians and their velocities and a predictor predicts the groups’ trajectories. Three robot motion modes, moving-solo, pedestrian-following, and courteous-stopping, are proposed based on a systematic analysis and classification of dense crowds and they are utilized to generate candidate local trajectories. A composite metric is proposed to determine the best local trajectory by simultaneously considering the robot motion safety, efficiency, and stability of the trajectory. The proposed robot navigation framework is evaluated both in simulations and real-world experiments with various scenarios. The results show that the proposed framework surpasses the state-of-the-art methods in terms of efficiency and social compliance. Note to Practitioners—This paper is motivated by the socially-compliant navigation problem of mobile robots in densely crowded environments such as hospitals and airports. Robots navigation in these scenes need to follow collective social conventions to ensure ordered and efficient public pedestrian traffics, and individual social conventions for acceptance by human society. Existing approaches generally consider only one type of social conventions cannot accomplish a truly socially-compliant navigation and cannot be naturally accepted in the human pedestrian environment. This paper proposes to imitate the pedestrian’s walking behavior for robot navigation by naturally choosing one of the three walking modes, moving-solo, following, and courteous-stopping. We construct the candidate trajectory sampling strategies for different motion modes based on the global path and the two types of social conventions. Experimental results show that the proposed framework can be applied in large-scale, densely crowded, unstructured, and human-robot coexisting environments for socially-compliant navigation. However, the proposed navigation framework has limited improvement in navigation efficiency in a highly crowded environment due to the limited feasible space. Even pedestrians are difficult to walk in these highly crowded environments. In future work, we will study how to auto-adjust the weights to steer the robot to efficient navigation in highly crowded environments.

Mobile robots path planning and mobile multirobots control: A review

AbstractMobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.

Turning Point and Free Segments Strategies for Navigation of Wheeled Mobile Robot

The basic idea of the developed work is to solve the problem of mobile robot navigation with obstacle avoidance and the trajectory tracking problem in simple and complex environments. The research contribution aims to develop a strategy of navigation based on the turning point and the free segments algorithms. Indeed, a turning point method is developed in order to solve the problem of navigation in a simple environment. Then, the free segments approach is applied in order to solve the problem of obstacle avoidance in a complex environment. The second part of this paper aims to solve the problem of trajectory tracking. For this reason, a sliding mode controller is proposed as a solution to control the stability of the mobile robot. Finally, some simulation results which are developed using Matlab software are given to prove the validity of the developed work.

Approximate Quadratic Programming Algorithm for Nonlinear Model Predictive Tracking Control of a Wheeled Mobile Robot

Nonlinear model predictive control (NMPC) has proved its competency in controlling constrained nonlinear processes. Although NMPC can achieve exemplary tracking performance, the related computation effort as well as guaranteeing tracking convergence are its main drawbacks. Indeed, constrained NMPC is a nonlinear and nonconvex optimization problem where it is difficult to find a feasible solution within a reasonable time. Motivated by these difficulties, this paper proposes a procedure, using Euler approximation, to transform the nonlinear optimization problem of NMPC into a constrained quadratic optimization problem. The proposed tracking controller is applied to the autonomous navigation problem of a wheeled mobile robot (WMR) in a constrained space. Under certain assumptions, we prove the closed-loop system stability and the boundedness of the tracking error. Further, we show the recursive feasibility of the solution. Simulations are performed, first to determine the adequate control parameters, and secondly to show the effectiveness of the proposed algorithm, while its real-time implementation is experimentally verified using a differential drive mobile robot.

Research on Mobile Robot Vision Navigation Algorithm

Robot navigation is one of the key technologies of indoor robots and it is the primary solution to the problem of intelligent robot. In this paper, the visual navigation problem of mobile robot was analyzed and the robot vision navigation algorithm based on monocular vision was proposed. The algorithm obtained three-dimensional information of obstacle through data fusion, builder a three-dimensional model of obstacle, and optimized the navigation path by using the heuristic best search algorithm, and realized the drawing of the robot navigation path.

Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg‐Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real‐time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real‐time localizing of the restaurant service robot.

An hybridization of global-local methods for autonomous mobile robot navigation in partially-known environments

This paper deals with the navigation problem of an autonomous non-holonomic mobile robot in partially-known environment. In this proposed method, the entire process of navigation is divided into two phases: an off-line phase on which a distance-optimal reference trajectory enables the mobile robot to move from an initial position to a desired target which is planned using the B-spline method and the Dijkstra algorithm. In the online phase of the navigation process, the mobile robot follows the planned trajectory using a sliding mode controller with the ability of avoiding unexpected obstacles by the use of fuzzy logic controller. Also, the fuzzy logic and fuzzy wall-following controllers are used to accomplish the reactive navigation mission (path tracking and obstacle avoidance) for a comparative purpose. Simulation results prove that the proposed path planning method (B-spline) is simple and effective. Also, they attest that the sliding mode controller track more precisely the reference trajectory than the fuzzy logic controller (in terms of time elapsed to reach the target and stability of two wheels velocity) and this last gives best results than the wall-following controller in the avoidance of unexpected obstacles. Thus, the effectiveness of our proposed approach (B-spline method combined with sliding mode and fuzzy logic controllers) is proved compared to other techniques.

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.

Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.

A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot

In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal path. As well as, PSO algorithm is used to find and tune on-line the neural control gains values of the nonlinear neural controller to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results by matlab showed that the proposed cognitive system is more accurate in terms of planning reference path to avoid obstacles and online finding and tuning parameters of the controller which generated smoothness control action without saturation state for tracking the reference path equation as well as minimize the mobile robot tracking pose error to zero value.

Mobile robot navigation based on tangent circle algorithm

A difficult issue in mobile robot navigation or path planning in static or dynamic environment is to find the path from the starting point to the target avoiding collisions with obstacles. This paper presents a new approach for solving the problem of mobile robot navigation in dynamic environment based on tangent circle algorithm. To plan the trajectory of the robot, the algorithm relies on the creation of different types of paths between the robot and the target. In fact, during the motion path, the robot switches between a straight line and/or circular trajectory in order to avoid obstacles and reach the goal. The proposed approach is implemented on different simulation scenarios involving state or dynamic obstacles in the environment. Results show that the novel method is efficient and that the mobile robot succeeds to reach its target in a collision-free path in different cases.

THE ALGORITHM OF SIMULTANEOUS NAVIGATION AND MAPPING FOR MOBILE ROBOT BASED ON THE ITERATIVE ALGORITHM OF THE NEAREST PIXELS AND DESCRIPTOR CALCULATED IN A CIRCULAR MOVING WINDOW

New combined algorithm for simultaneous navigation and map construction is developed using visual characteristics and depth information to compare images, register 3D-point clouds, and build global sequential 3D-maps of the surrounding space. The performance and computational complexity of the proposed RGB-D SLAM algorithm are presented and discussed with reference and real data. The results can be applied in real-time tracking of objects, in non-cooperative remote observation, and semantic mapping of mobile robot navigation problems.

미지 동적 환경에서 다중 이동로봇의 GA-Fuzzy 기반 자율항법

The work present in this paper deals with a navigation problem for multiple mobile robots in unknown indoor environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots’ bodies must be used to detect information about the surroundings. The environments simulated in this work are dynamic ones which contain not only static but also moving obstacles. In order to guide the robot to move along a collision-free path and reach the goal, this paper presented a navigation method based on fuzzy approach. Then genetic algorithms were applied to optimize the membership functions and rules of the fuzzy controller. The simulation results verified that the proposed method effectively addresses the mobile robot navigation problem.

Autonomous Navigation for Multiple Mobile Robots under Time Delay in Communication

This paper addresses the navigation problem for mobile robots under uncertain working conditions. It is assumed that the environment is unknown at the beginning of any experiment and that a visual feedback module supports the navigation strategy to make a set of robots achieve a goal in an environment with obstacles. The proposed navigation algorithm depends on the obstacle localization, and it is based on visibility conditions of the goal and control points that are defined from the geometric distribution of the environment. The problem has been widely addressed in the literature; however, in this work it is solved by means of a low cost vision system which incorporates a natural restriction in the rate of capture. This fact limits the maximal velocity of each robot, therefore high accelerations imply that the robot could not be detected in time, affecting the control system stability. This high acceleration problem is addressed by means of delay compensation on the communication channel with a scattering transformation strategy that guarantees to reach the goal position, allowing the robot to perform high velocities if needed. Real time experiments are developed to validate the effectiveness of the proposed strategy.

Improving mobile robot navigation by combining fuzzy reasoning and virtual obstacle algorithm

In this paper, a new approach is developed for contributing in solving the problem of autonomous mobile robot navigation in unknown environments. This approach is built upon combining fuzzy reasoning and virtual obstacle algorithm to overcome the local minimum problem encountered in presence of concave obstacles by efficiently coordinating priorities between multiple reactive behaviors such as goal reaching, obstacles avoiding, wall following and emergency situations preventing. To achieve this objective, an array of ultrasonic sensors is mounted on the mobile robot providing the distance information between the robot and obstacles. This distance information is used by the virtual obstacle algorithm to calculate some sub-goals for determining the good motion direction to avoid robot trap in local region (emergency situation), since the fuzzy reasoning is used for behavior control of the mobile robot. All the reactive behaviors are mapped into one universe of discourse to guarantee a smooth transition between them especially when the robot moves through closely spaced obstacles. In this manner, the robot oscillations are significantly reduced. Some simulation results are presented to show the ability of the developed approach in performing successfully in complex and uncertain environments.