Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance

This paper considers the real-time navigation and obstacle avoidance of mobile robots in complex environments. A novel local environment perception and navigation framework for mobile robot navigation based on the elevation information is proposed. A local elevation map is introduced to perceive and map obstacles in the local environment. Calculating obstacles’ heights and shapes by local elevation maps, the terrain traversability of the surrounding environment of a robot is determined, and a two-dimensional local occupancy grid map for robot path planning and navigation in local environment is generated. The effectiveness of our approach is validated experimentally through real-time navigation and obstacle avoidance in the artificial environment. The experimental results show that using the proposed framework, a mobile robot can safely reach its target position while avoiding the non-crossable area. For indoor mobile robots with limited computing power, in complex environments, it could provides more safe navigation and path-planning.

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

Intelligent robotics is gaining significance in Maintenance, Repair, and Overhaul (MRO) hangar operations, where mobile robots navigate complex and dynamic environments for Aircraft visual inspection. Aircraft hangars are usually busy and changing, with objects of varying shapes and sizes presenting harsh obstacles and conditions that can lead to potential collisions and safety hazards. This makes Obstacle detection and avoidance critical for safe and efficient robot navigation tasks. Conventional methods have been applied with computational issues, while learning-based approaches are limited in detection accuracy. This paper proposes a vision-based navigation model that integrates a pre-trained Yolov5 object detection model into a Robot Operating System (ROS) navigation stack to optimise obstacle detection and avoidance in a complex environment. The experiment is validated and evaluated in ROS-Gazebo simulation and turtlebot3 waffle-pi robot platform. The results showed that the robot can increasingly detect and avoid obstacles without colliding while navigating through different checkpoints to the target location.

Purpose This review paper aims to provide a comprehensive review of Dubins robot path planning, which has become increasingly important in mobile robotics, autonomous vehicles and industrial applications. The authors identify key developments, analyze existing approaches and highlight the fundamental constraints, algorithms and future opportunities involving curvature-constrained, forward-only path planning. Design/methodology/approach The review consists of three parts: basic Dubins theory, seven decades of development and future opportunities. In the first part, the authors summarize problem formulations and representative computational strategies/algorithms. In the second part, the seventy years of development are divided into four stages, with each stage summarizing developments in important topics such as point-to-point planning, obstacle avoidance and variable speed planning. In addition, the authors analyze current challenges and evaluate recent extensions in cooperative, real-time and learning-based planning frameworks. Findings Dubins path planning has evolved from simple analytical models to complex multirobot and dynamic scenarios. While exact solutions exist for basic tasks, the scalability, obstacle constraints and real-time responsiveness remain active challenges. Promising future directions include integrating machine learning for adaptive planning, and extending to Reeds–Shepp and nonholonomic systems in three-dimensional environments. Originality/value This review offers a structured and in-depth overview of path planning for Dubins robots, connecting core theoretical concepts with the latest developments and real-world implementations. Unlike prior surveys that roughly treat general nonholonomic planning or sampling-based methods, this paper for the first time emphasizes the curvature-constrained planning, especially under the Dubins constraints. By identifying underexplored areas such as dynamic environments, real-time applications, this review offers actionable insights and future research directions valuable to both researchers and practitioners in mobile and industrial robots.

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.

Mobile robot has been a major role to the application in military, industrial and agricultural purposes. Mobile robot should navigate through desire route and avoid the obstacle within the path. Many researcher come with the solution by using the various type of control and instrumentation system. The complexity of mobile robot system can make the system cost intensive and high risk. Proposed is a mobile robot equipped with GPS navigation system and obstacle avoidance system with low cost mobile structure, GPS module and sonar sensor. The combination GPS and Sonar will determine the position and obstacle avoidance for the mobile robot. Mobile robot should navigate according to waypoint that preset to the GPS module and sonar sensor detects the obstacle during mobile robot navigation by triggering the sonar sensor in sequence by using commanded loop daisy chaining application method. Mobile Robot can navigate through desired waypoint and at the same time apply the obstacle avoidance rules

Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.

Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.

The article considers the problem of navigating mobile robots and finding the best way to the goal in real-time in a space surrounded by unknown objects. The motor actions of the robot must be defined and adapted to changes in the environment. When using only laser scanners on mobile work, objects above or below the lasers' level will remain obstacles to the robot. Current algorithms and principles of navigation are considered. Extended the existing real-time interference detection system using lasers by adding a camera that calculates the length of objects. The new system has been successfully implemented and tested in a mobile robot, ensuring the passage of the road providing collision-free paths. The obtained simulation results are presented in the article. The existing problems of navigation of mobile robots, which are moving in the particular area from their position to the specified destination on the map, were investigated. The current problem is the inability to spot objects that are not on the same level as the mobile robot's lasers. Moreover, the task is complicated when you need to recognize such objects while the robot is moving in real time. The current algorithms and principles of navigation given by previous research and publications are analyzed. As a result of the work, the existing system of recognition and avoidance of obstacles was expanded. Prior to that, the system used only odometry and information obtained from laser scanners, without obtaining data from other sources of environmental information. The idea of development was to use a camera, which was already part of the components of the researched mobile robot. It has become possible to generate a pointcloud relative to the environment, using a depth sensing camera to calculate the distance to objects. Because the density of the received data in the form of a pointcloud is too high for further processing, a downsample VoxelGrid filter was used, which reduces the density of the point cloud. VoxelGrid belongs to the PCL library. Another problem was the removal of information about unnecessary objects in the camera's field of view. These include the floor, ceiling, parts of the robot (such as a manipulator). The PassThrough filter from the PCL library was used to solve this problem. The next step is to process the filtered data using OctoMap. As a result, an octree is generated. A top-down projection is created from the octree generated in the previous step. The resulting projection must be processed and converted into polygonal obstacles. Only then they will be marked by teb_local_planner as obstacles. The developed system was successfully implemented and tested both in the Gazebo simulation and in the researche mobile robot. The path with obstacles will be completed without collisions. The paper presents the obtained test results.

Mobile Robot Navigation and Obstacles Avoidance based on Planning and Re-Planning Algorithm

A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.