Navigation Method For Mobile Robot Research Articles

Visual navigation method for agricultural mobile robots based on spatial continuity clustering algorithm

Visual navigation method for agricultural mobile robots based on spatial continuity clustering algorithm

Online Navigation Method for Mobile Robot Based on Thermal Compliance

Online Navigation Method for Mobile Robot Based on Thermal Compliance

Practical Implementation of Visual Navigation Based on Semantic Segmentation for Human-Centric Environments

This study focuses on visual navigation methods for autonomous mobile robots based on semantic segmentation results. The challenge is to perform the expected actions without being affected by the presence of pedestrians. Therefore, we implemented a semantics-based localization method that is not affected by dynamic obstacles and a direction change method at intersections that functions even with coarse-grain localization results. The proposed method was evaluated through driving experiments in the Tsukuba Challenge 2022, where a 290 m run including 10 intersections was achieved in the confirmation run section.

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera's field of view. A single monocular sensor's sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system's dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera's field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera's field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

Predictive reinforcement learning: map-less navigation method for mobile robot

Predictive reinforcement learning: map-less navigation method for mobile robot

Fast Lidar Inertial Odometry and Mapping for Mobile Robot SE(2) Navigation

This paper presents a fast Lidar inertial odometry and mapping (F-LIOM) method for mobile robot navigation on flat terrain with high real-time pose estimation, map building, and place recognition. Existing works on Lidar inertial odometry have mostly parameterized the keyframe pose as SE(3) even when the robots moved on flat ground, which complicated the motion model and was not conducive to real-time non-linear optimization. In this paper, F-LIOM is shown to be cost-effective in terms of model complexity and computation efficiency for robot SE(2) navigation, as the motions in other degrees of freedom in 3D, including roll, pitch, and z, are considered to be noise terms that corrupt the pose estimation. For front-end place recognition, the smoothness information of the feature point cloud is introduced to construct a novel global descriptor that integrates geometry and environmental texture characteristics. Experiments under challenging scenarios, including self-collected datasets and public datasets, were conducted to validate the proposed method. The experimental results demonstrated that F-LIOM could achieve competitive real-time performance in terms of accuracy compared with state-of-the-art counterparts. Our solution has significant superiority and the potential to be deployed in limited-resource mobile robot systems.

Turning at Intersections Using Virtual LiDAR Signals Obtained from a Segmentation Result

We implemented a novel visual navigation method for autonomous mobile robots, which is based on the results of semantic segmentation. The novelty of this method lies in its control strategy used for a robot during road-following: the robot moves toward a target point determined through semantic information. Previous implementations of the method sometimes failed to turn at an intersection owing to a fixed value of the turning angle. To address this issue, this study proposes a novel method for turning at an intersection using a control method based on a target point, which was originally developed for road-following. Here, an intersection is modeled as consisting of multiple straight roads. Evaluation using the CARLA simulator showed that the proposed method could accurately estimate the parameters representing a virtual road composing an intersection. In addition, run experiments conducted at the Ikuta Campus of Meiji University using an actual robot confirmed that the proposed method could appropriately make turns at intersections.

Using Big Data Technology to Improving Navigation Methods for Mobile Robots

Using Big Data Technology to Improving Navigation Methods for Mobile Robots

Using of BIG DATA technology to improving navigation methods for mobile robots

Using of BIG DATA technology to improving navigation methods for mobile robots

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

PurposeWheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments.Design/methodology/approachGiven a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR.FindingsThe authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments.Originality/valueIn this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

LIDAR-based autonomous navigation method for an agricultural mobile robot in strawberry greenhouse: AgriEco Robot

<abstract> <p>This paper presents an autonomous navigation method for an agricultural mobile robot "AgriEco Robot", with four-wheel-drive and embedded perception sensors. The proposed method allows an accurate guidance between strawberry crop rows while automatically spraying pesticides, as well as detecting the end and switching to the next rows. The main control system was developed using Robot Operating System (ROS) based on a 2D LIDAR sensor. The acquired 2D point clouds data is processed to estimate the robot's heading and lateral offset relative to crop rows. A motion controller is incorporated to ensure the developed autonomous navigation method. Performance in terms of accuracy of the autonomous navigation has been evaluated in real-world conditions within strawberry greenhouses, proving its usefulness for automatic pesticide spraying.</p> </abstract>

Prm Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

Prm Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

Multispectral Visual Odometry Using SVSF for Mobile Robot Localization

In this paper, we propose a novel method for mobile robot localization and navigation based on multispectral visual odometry (MVO). The proposed approach consists in combining visible and infrared images to localize the mobile robot under different conditions (day, night, indoor and outdoor). The depth image acquired by the Kinect sensor is very sensitive for IR luminosity, which makes it not very useful for outdoor localization. So, we propose an efficient solution for the aforementioned Kinect limitation based on three navigation modes: indoor localization based on RGB/depth images, night localization based on depth/IR images and outdoor localization using multispectral stereovision RGB/IR. For automatic selection of the appropriate navigation modes, we proposed a fuzzy logic controller based on images’ energies. To overcome the limitation of the multimodal visual navigation (MMVN) especially during navigation mode switching, a smooth variable structure filter (SVSF) is implemented to fuse the MVO pose with the wheel odometry (WO) pose based on the variable structure theory. The proposed approaches are validated with success experimentally for trajectory tracking using the mobile robot (Pioneer P3-AT).

Application of Neural Network and Computer in Intelligent Robot

Autonomous mobile robot is widely used in security patrol, freight, autopilot, and family life and other fields, improve the application effect is one of the most important factor is to be able to accurate navigation path, so how to design a highly efficient, intelligent autonomous mobile robot navigation path algorithm has been a popular research topic in the field. This paper mainly studies the application of the combination of neural network and computer in the field of intelligent robot. The Pninnet model is used to simulate the 3D simulation environment built in this paper, and the path navigation method of the autonomous mobile robot based on the end-to-end model is realized, and an algorithm based on ray detection is proposed to achieve obstacle avoidance. Through several groups of comparative experiments (such as comparison with Pilotnet model, etc.) and analysis, it is proved that the algorithm in this paper improves the efficiency of robot navigation research and the robot developed has a better performance in navigation in unknown or familiar environments.

A Behavior-Based Mobile Robot Navigation Method with Deep Reinforcement Learning

Deep reinforcement learning-based mobile robot navigation has attracted some recent interest. In the single-agent case, a robot can learn to navigate autonomously without a map of the environment. In the multi-agent case, robots can learn to avoid collisions with each other. In this work, we propose a behavior-based mobile robot navigation method which directly maps the raw sensor data and goal information to the control command. The learned navigation policy can be applied in both single-agent and multi-agent scenarios. Two basic navigation behaviors are considered in our method, which are goal reaching and collision avoidance. The two behaviors are fused based on the risk-level estimation of the current state. The navigation task is decomposed using the behavior-based framework, which is capable of reducing the complexity of the learning process. The simulations and real-world experiments demonstrate that the proposed method can enable the collision-free autonomous navigation of multiple mobile robots in unknown environments.

Indoor Vision/INS Integrated Mobile Robot Navigation Using Multimodel-Based Multifrequency Kalman Filter

In order to further improve positioning accuracy, this paper proposes an indoor vision/INS integrated mobile robot navigation method using multimodel-based multifrequency Kalman filter. Firstly, to overcome the insufficient accuracy of visual data when a robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels which can make fixed point angled turns. Secondly, a multifrequency Kalman filter has been used to fuse the position information from both the inertial navigation system and the visual navigation system, which overcomes the problem that the filtering period of the integrated navigation system is too long. The proposed multimodel multifrequency Kalman filter gives the root mean square error (RMSE) of 0.0184 m in the direction of east and 0.0977 m in north, respectively. The RMSE of visual navigation system is 0.8925 m in the direction of east and 0.9539 m in north, respectively. Experimental results show that the proposed method is effective.

A Novel Mobile Robot Navigation Method Based on Hand-Drawn Paths

In the real world, a human tourist can quickly arrive at a target along the route planned by his/her instinctive cognition of prepared maps. While on the mobile robot, it is required to know every way-points of the static or dynamic planning paths to reach a goal point. The previous navigation modes are mostly from sensor location, speech interaction, grid maps and so on. They rely on sensing performance, the speech instruction of site personnel or complex algorithms for the path searching and tracking. For improving the performance of interaction and tele-operated path tracking, in this paper, a novel interactive navigation based on the recognition of hand-drawn paths is proposed for mobile robots. Pen, paper, and camera are employed as the interface between users and the mobile robot. Initially, the image of a hand drawn path in the paper map is captured by a camera and segmented from the background by the color detection model. It is hypothesized that the captured frame of hand-drawn paths is slant to the original orthogonal map. Therefore, this method should firstly project the smoothed skeleton of slant path into the coordinate space of orthogonal map to guide the robot to perform path tracking. Then the hand-drawn path is rectified and remapped into the registered reference image of the orthogonal map through feature matching and perspective transformation. The combination of SIFT and RANSAC algorithms are employed for improving the accuracy of the projection of slant paths in the orthogonal map. Sequentially, a limited number of way-points are picked out from the corrected path in an orthogonal 2D map space usable for 2D navigation. The oriented sequential motion vectors are estimated by linking all successive way-points and employed for the path tracking along the predefined route step by step. Eventually, random navigation paths are designed to validate the robustness, effectiveness and accuracy of the constructed interactive navigation system.

A Standalone RFID-Based Mobile Robot Navigation Method Using Single Passive Tag

This article proposes a standalone radio frequency identification (RFID)-based mobile robot navigation method, in which a mobile robot equipped with reader antennas can be continuously guided to a static object marked with a single passive UHF RFID tag. An observation model based on the RFID phase difference is built and integrated into a particle filter, by which the instantaneous relative position between the mobile robot and the tagged object can be detected in real time. Based on the position information extracted from the RFID system, the mobile robot adjusts its pose to move toward the RFID-tagged object. Compared with the existing RFID-based mobile robot navigation methods, the proposed method requires no external sensors other than the RFID and requires only a single passive tag. Experiments using commercial off-the-shelf (COTS) RFID devices are performed, and the results indicate that the mobile robot can satisfactorily realize navigation task with a distance accuracy of 4.04 cm and a bearing accuracy of 2.23°. The proposed method is well applicable for the navigation scenes in which the absolute position of the tagged target object is not known beforehand. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —UHF radio frequency identification (RFID) has been widely applied as an asset management ID sensor in many fields. RFID-based mobile robot navigation technology can further increase its application value as a location sensor. This article proposes a standalone RFID-based mobile robot navigation method, in which the reference tag and the external sensors other than RFID are both not required. In the proposed method, only a single passive tag is attached to the static target object. Experimental results indicate that the proposed method can enable navigation task with good performance in situations in which the absolute navigation goal position is not known in advance.

A novel mobile robot navigation method based on deep reinforcement learning

At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.