Navigation Algorithm For Mobile Robots Research Articles

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Autonomous navigation of mobile robots in unknown environments using off-policy reinforcement learning with curriculum learning

Reinforcement learning (RL) is effective for autonomous navigation tasks without prior knowledge of the environment. However, traditional mobile robot navigation algorithms, based on off-policy RL, often face challenges such as low sample efficiency during training and lack of adequate safety mechanisms. In this paper, we present an off-policy RL navigation model named Soft Actor-Critic with Curriculum Prioritization and Fuzzy Logic (SCF). The model uses energy as a prioritized evaluation metric for experience replay. And through task-level curriculum, the agent’s learning sequence is formulated, thereby enhancing sampling efficiency and safety. We propose a Curriculum-based Energy Prioritization (CEP) approach. It selects a replay trajectory that matches the current agent’s capability based on trajectory energy. Our results show that robots using off-policy RL often have limitations in dynamic obstacle avoidance. To rectify this, our model uses a fuzzy logic controller to enhance real-time obstacle avoidance. The SCF approach enables mobile robots to navigate adeptly in unpredictable and dynamic environments, ensuring optimal planning control while being safe and robust. Experiments in Gazebo simulation environment and real world confirm the effectiveness of our proposed method. The comparison results show the superior performance of this method, especially in unknown and dynamic environments.

Novel Fuzzy Reinforcement Algorithm for Mobile Robot Navigation in Automated Storage

Novel Fuzzy Reinforcement Algorithm for Mobile Robot Navigation in Automated Storage

Sequential batch fusion magnetic anomaly navigation for a low-cost indoor mobile robot

This paper describes a new magnetic map-matching algorithm structure based on sequential batch fusion. The proposed algorithm enables real-time computation and more stable indoor localization of mobile robots. Conventional magnetic map-matching-based localization has the disadvantage that real-time navigation is not possible when using a batch process, and navigation fails or has significant localization errors owing to similar magnetic field distortion. This study proposes that measurement updates should be performed based on the convergence of weights with several grid points by repeatedly updating the weights of the grid points using magnetic field similarity. The performance of the proposed navigation algorithm was verified using an open dataset, and the proposed technique had a position error of 1 m even in a path in which navigation using the conventional method failed. It provides an indoor mobile robot navigation algorithm that is significantly cheaper than other conventional sensor-based indoor navigation systems.

Energy Consumption Analysis of the Selected Navigation Algorithms for Wheeled Mobile Robots

The article presents the research on navigation algorithms of a wheeled mobile robot with the use of a vision mapping system and the analysis of energy consumption of selected navigation algorithms, such as RRT and A-star. Obstacle maps were made with the use of an RGBW camera, and binary occupation maps were also made, which were used to determine the traffic path. To recreate the routes in hardware, a programmed Pure Pursuit controller was used. The results of navigation were compared on the basis of the forward kinematics model and odometry measurements. Quantities such as current, except (x, y, phi), and linear and angular velocities were measured in real time. As a result of the conducted research, it was found that the RRT star algorithm consumes the least energy to reach the designated target in the designated environment.

Agoraphilic Navigation Algorithm Under Dynamic Environment

This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article.

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, pathplanning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot pathplanning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field.

Determining Shape and Size of Personal Space of a Human when Passed by a Robot

Autonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results give us more insight into the shape and size of personal space in human–robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals.

A Fuzzy Analytic Hierarchy Process and Cooperative Game Theory Combined Multiple Mobile Robot Navigation Algorithm.

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout.

Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm

Over the past few years, mobile robots are widely used in various industries because they can navigate in dynamic environments and carry out everyday tasks efficiently. Path planning undoubtedly plays an important role in mobile robot navigation, thus becoming one of the most researched topics in the field of robotics. The metaheuristic algorithms have been extensively studied in recent years to solve the path planning problems the same way as the optimization problems were solved, or in other words, plan the optimum path for the robot to navigate from the starting point to the destination. In this research, a mobile robot is launched to find its path from the starting point to the destination in three different simulated environments using a proposed hybrid metaheuristic algorithm between Particle Swarm Optimization and Fringe Search Algorithm, named PSOFS, and its simultaneous localization and mapping (SLAM) capability. During runtime, the path is optimized by considering the path length. The performance of PSOFS for local path planning is compared against two existing algorithms by evaluating the path smoothness as well as robot safety. The results showed that PSOFS has successfully generated shorter, smoother, and safer paths than the algorithms for mobile robot navigation in unknown indoor environments.

Interactive model of magnetic field reconstruction stand for mobile robot navigation algorithms debugging which use magnetometer data

An important addition to inertial navigation systems is the magnetometer. Areas with magnetic field anomalies serve to determine the reference points. However, magnetometers can be influenced by both the robot’s configuration and its electrical equipment. Compensation for the robot’s self-influence on the readings of the magnetometers is carried out by computer tools. In order to obtain the initial data, live experiments are required in a natural environment. To simplify data acquisition concerning the behaviour of the magnetometric systems of a mobile robot, a special facility that allows for the local compensation of the Earth’s magnetic field is used, and an artificial magnetic field that varies according to a predetermined algorithm is created. Using this facility, we can also simulate the magnetic field that will be present in the intended environment of the application of the robot. The facility features are a working space that is sufficient to place the mobile robot; a coil temperature drift correction; uniformity of the frequency response in operating frequency range; compensation for the power supply interference and similar disturbances; sensitivity equalisation of control channels; compensation for the misalignment of the sensor’s and coil system’s coordinate systems. An interactive Simulink model is designed and evaluated. The automated stand is created as an experimental facility, its parameters proving the proposed model’s adequacy.

Automatic control of mobile robot based on autonomous navigation algorithm

Autonomous navigation control is the key technology of mobile robot. The navigation algorithm of mobile robot is studied in this paper. A simultaneous localization and mapping (SLAM) algorithm based on particle filter is designed. Then, it is combined with VFH obstacle avoidance algorithm to obtain the navigation algorithm and conduct experiments on it. Through the simulation experiment in MATLAB environment, it is found that the use of SLAM algorithm can reduce the position error of the robot. The average error is 0.003 m, while the average position error without SLAM algorithm is about 0.009 m, which proves the reliability of SLAM algorithm. Then, the simulation experiment of the navigation algorithm also proves that the algorithm can avoid obstacles and reach the destination accurately. The research in this paper provides some theoretical references for the further development of autonomous navigation control of mobile robots.

KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots

The paper proposes an algorithm for mobile robot navigation that integrates the Gmapping proposal distribution with the Kullback–Leibler divergence for adapting the number of particles. This results in a very effective particle filter with adaptive sample size. The algorithm has been evaluated in both simulation and experimental studies, using the standard KLD—sampling MCL as a benchmark. Simulation results show that the proposed algorithm achieves higher localization accuracy with a smaller number of particles compared to the benchmark algorithm. In a more realistic scenario using experimental data and simulated robot odometry with drift, the proposed algorithm again has greater accuracy using a lower number of particles.

The [formula omitted] metric: A performance measure for collision avoidance algorithms

Despite the comprehensive development in the field of navigation algorithms for mobile robots, the research on performance metrics and evaluation procedures for making standardized quantitative comparison between different algorithms has gained attention only recently. This work attempts to contribute with such effort by introducing a performance metric for the assessment of collision avoidance algorithms for mobile robots. The proposed metric comprehensively evaluates the actions taken by the objects and their consequences, in a given scenario of any given collision avoidance algorithm, based on the concept of probability of collision. The contribution of the paper encompasses the definition of the metric, the methodology to estimate the metric, and the framework to apply the metric for any given scenario. Experiments and numerical simulations are conducted to validate and demonstrate the effectiveness of the proposed metric in performance evaluation and comparison among different collision avoidance algorithms.

Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions

Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors.

Path planning in uncertain environment by using firefly algorithm

Abstract Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm (FA) for Mobile Robot Navigation (MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.

Fuzzy logic controllers design for omnidirectional mobile robot navigation

This paper presents a new design approach for an intelligent navigation algorithm for omnidirectional mobile robots. Unlike the previous works dealing with the navigation of omnidirectional robots that are focused on only the estimation of the final position, the main contribution of the present study is summed up in the fact that the robot has to reach the final desired position with a predefined final steering angle. This latter improvement is a part of researches carried out on Intelligent Transport Systems (ITS). Taking into consideration the drawbacks of proportional integral (PI) control when applied to omnidirectional robot navigation, we develop an approach to design a fuzzy logic PI controller (Fuzzy-PI).Preliminary simulation and experimental results using the Fuzzy-PI controller have shown limitations as the robot performed a larger path when the desired final angle increased. Thus, a deepen study have concluded that the Fuzzy-PI system cannot control at the same time the linear and angular velocities. To overcome these drawbacks, we propose to replace the previous intelligent navigation system by two independent controllers. The designed Fuzzy-PI controllers can adjust their parameters (KP, KI) to reduce the error caused by the dynamic changes and navigation challenges of omnidirectional robot. A navigation algorithm cannot be efficient without obstacle avoidance system. To achieve this goal, we have developed a third fuzzy controller to fulfill this task.To evaluate the real performances of fuzzy controllers for navigation and obstacles avoidance, simulation and experimental tests are performed for one, two and three obstacles. The obtained results confirm that the omnidirectional robot could navigate in an unstructured and unknown obstacles environment with better performances and efficiency.

Robust 2D map building with motion-free ICP algorithm for mobile robot navigation

SUMMARYA new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

Prediction-Based Path Planning with Obstacle Avoidance in Dynamic Target Environment

In this paper, a new algorithm for mobile robot navigation and polygonal obstacles avoidance in dynamic target environment is introduced. In the dynamic target path planning theagent (robot) trying to reach a moving target in minimum pathcost. The introduced algorithm which called Prediction-basedpath planning with obstacle avoidance in dynamic target environment planning a path to a moving target by predicting the nexttarget location, then computing a path from the robot current location to the predicted target location representing each visibleobstacle by the smallest circle that enclosing the polygon obstacle,then determine the visible tangents between the robot and the circular obstacle that intersect its shortest path and compute theshortest path. Three target movement scenarios were suggestedand tested in different environment conditions. The results showthat the target was reached in all scenarios and under all environment conditions with good path cost.

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

Purpose This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.