Autonomous Navigation Technology Research Articles

Research on Autonomous Navigation and Intelligent Recognition Technology of Ship Hull Cleaning Robots Based on Deep Learning

Research on Autonomous Navigation and Intelligent Recognition Technology of Ship Hull Cleaning Robots Based on Deep Learning

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

A Path Planning System for Orchard Mower Based on Improved A* Algorithm

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments.

АВТОНОМНІ НАДВОДНІ ДОПОМІЖНІ СУДНА: ПЕРЕВАГИ ТА ПРОБЛЕМИ

With the continuous progress of modern science and technology and the increasing requirements for marine transport in various fields, the intelligence and automation of ships has become a general trend. Autonomous unmanned surface vessel (UASV) control generally includes UASV trajectory planning, path tracking control, and autonomous collision avoidance control. Due to their low cost, small size, fast action, reconnaissance capabilities and other advantages, BPNS play a very important role in everyday life, emergency response and scientific research. UASV, as a rule, consist of a platform and a payload system. But in the whole process of UASV navigation, autonomous mooring is also an important part. And there are fewer studies on the automatic mooring algorithm of UASV. The advanced technology of autonomous mooring of BPNS can effectively reduce the cost of human, material and financial resources, while reducing the accident rate in a reasonable and safe manner. Therefore, it is very important to comprehensively promote the development of autonomous navigation and mooring technology of UASV. Keywords: Autonomous unmanned surface vessel (UASV), autonomous mooring.

Development and Experiment of an Innovative Row-Controlled Device for Residual Film Collector to Drive Autonomously along the Ridge.

The field harvesting process of harvesting machinery is often affected by high workload and environmental factors that can impede/delay manual rowing, thereby leading to lower efficiency and quality in the residual film collector. To address this challenge, an automatic rowing control system using the 4mz-220d self-propelled residual film collector as the experimental carrier was proposed in this study. Cotton stalks in the ridges were chosen as the research object, and a comprehensive application of key technologies, machinery, and electronic control was used, thereby incorporating a pure tracking model as the path-tracking control method. To achieve the automatic rowing function during the field traveling process, the fuzzy control principle was implemented to adjust the forward distance within the pure tracking model dynamically, and the expected steering angle of the steering wheel was determined based on the kinematic model of the recovery machine. The MATLAB/Simulink software was utilized to simulate and analyze the proposed model, thus achieving significant improvements in the automation level of the residual film collector. The field harvesting tests showed that the average deviation of the manual rowing was 0.144 m, while the average deviation of the automatic rowing was 0.066 m. Moreover, the average lateral deviation of the automatic rowing was reduced by 0.078 m with a probability of deviation within 0.1 m of 95.71%. The research study demonstrated that the designed automatic rowing system exhibited high stability and robustness, thereby meeting the requirements of the autonomous rowing operations of residual film collectors. The results of this study can serve as a reference for future research on autonomous navigation technology in agriculture.

Design of Bluetooth 5.1 Angle of Arrival Homing Controller for Autonomous Mobile Robot

With the improvement of autonomous robot navigation technologies, mobile robots can now be deployed in uncertain, real-world environments. An aspect of autonomous robot navigation in such scenarios is the capability to navigate to a real-time determined (previously unknown) location anywhere in its vicinity. This is especially pertinent for indoor navigation where existing localization technologies such as GPS do not provide sufficient accuracy of target location. In this paper, a controller design is proposed which homes a mobile robot to an object of unknown location using Bluetooth 5.1 Angle of Arrival (AoA) technology. The proposed setup consists of a target object with a Bluetooth beacon and a single Bluetooth antenna array mounted on a mobile robot. The controller uses a hybrid approach to calculating and updating the estimated target position by implementing parallax and vector position calculations from AoA and RSSI Bluetooth data. Simulations with various levels of sensor noise showed convergence to accurate target positions (mean accuracy of 0.12 m or less) in both obstacle-free and obstacle-present environments. The controller can be implemented as a standalone controller by directly commanding robot motion toward the target, or it can integrate with other existing robot navigation techniques by outputting a target position.

P‐2.10: Design and Implementation of Mobile Robot Path Planning System Based on SLAM Technology

The exploration of unknown environment for mobile unmanned vehicles has always been a hot and difficult research topic. Localization and map construction (SLAM) and path planning scheme are the key technologies for autonomous localization and navigation of unmanned vehicles. Path planning of mobile unmanned vehicles is one of the most important core technologies of unmanned vehicles, and the planned path should be as optimal and conflict-free as possible. This paper analyzes and compares the global path planning techniques based on Dijkstra and A star. A star algorithm introduces heuristic functions and reduces the search of nodes, which not only retains the advantages of Dijkstra algorithm in obtaining the shortest path, but also takes into account the advantages of breadth first search. Therefore, A star algorithm is selected as the global path planning algorithm in this topic.

3-D Path-Following Control for Steerable Needles With Fiber Bragg Gratings in Multi-Core Fibers.

In this study, a closed-loop, 3-D path-following controller for steerable needles is presented. The control loop is closed via the feedback from FBG-inscribed MCFs embedded within the needle. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, is used as the basis for the guidance method. To handle needle-tissue interactions, we propose using Active Disturbance Rejection Control (ADRC) because of its robustness within hard-to-model environments. We investigate both linear and nonlinear ADRC, and validate the approach with a Programmable Bevel-tip Steerable Needle (PBN) in both phantom tissue and ex vivo brain, with some of the experiments involving moving targets. The mean, standard deviation, and maximum absolute position errors are observed to be 1.79 mm, 1.04 mm, and 5.84 mm, respectively, for 3-D, 120 mm deep, path-following experiments. MCFs with FBGs are a promising technology for autonomous steerable needle navigation, as demonstrated here on PBNs. FBGs in MCFs can be used to provide effective feedback in path-following controllers for steerable needles.

Polarization Orientation Method Based on Remote Sensing Image in Cloudy Weather

Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology.

Pose Detection and Automatic Deviation Correction Control Strategy of Crawler Walking Equipment in Coal Mines

Autonomous navigation technology is the basis of underground crawler walking equipment automation. Based on research of the sector laser pose parameter detection method, a pose detection system based on a cross laser is proposed. The mathematical model between pose parameters, laser receiver measurement data and tilt sensor measurement data is established by vector algorithm and the expression of key pose parameters is deduced. This paper studies the deviation correction control strategy of crawler walking equipment and puts forward the point deviation correction control strategy based on roadway excavation technology. Firstly, the end position of the path is determined according to the initial pose of the roadheader. Then, Bessel curve is used to plan the path between the starting point and the endpoint, and the pure tracking algorithm is used to track the planned path. The measurement errors of X and Y coordinates and yaw angle of roadheader within 10 80 m shall not exceed 10 mm, 32 mm and 0.65°. Using the point deviation correction strategy, the positioning error of the roadheader at the end of the path is less than 13 mm, and the yaw angle error is less than 0.16°. In the deviation correction process, the maximum angular velocity of the roadheader is 0.07 rad/s, which is less than 0.82 rad/s of the conventional deviation correction strategy. The results show that the point deviation correction strategy can not only ensure the accuracy of motion control, but also improve the stability of equipment motion.

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

Robot Trajectory Planning Based on the Energy Management Strategy

With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.

SA‐BiSeNet: Swap attention bilateral segmentation network for real‐time inland waterways segmentation

The technology for autonomous navigation on inland waterways is worth investigating, and navigable water surface segmentation is a key part of this technology. Semantic segmentation methods based on deep learning are able to distinguish between water surface areas and non-water surface areas. However, existing semantic segmentation methods cannot meet the requirements of the water surface segmentation task in terms of both segmentation precision and real-time performance. In this study, a Swap Attention Bilateral Segmentation Network (SA-BiSeNet) is proposed to improve segmentation performance while ensuring model inference speed by better fusing the two features of the dual-branch down-sampling network using the attention mechanism. Specifically, an innovative Swap Attention Module is designed to model the dependency between the features of the spatial detail branch and the features of the semantic branches, thus expanding the receptive fields of the spatial detail and semantic branches to each other's global contexts. This design can effectively fuse features and thus enhance feature representation. Experiments were conducted on the inland waterway dataset USVInland to verify the performance of SA-BiSeNet in terms of segmentation precision and inference speed, and SA-BiSeNet achieved 93.65% Mean IoU and maintained the same level of fps as the baseline.

Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.

Autonomous Navigation Technology for Low-Speed Small Unmanned Vehicle: An Overview

In special locations (scenes) such as campuses and closed parks, small unmanned vehicles have gained more attention and application. Autonomous navigation is one of the key technologies of low-speed small unmanned vehicles. It has become a research hotspot, but there are still many problems, such as perception sensitivity, navigation, and positioning accuracy, motion planning accuracy, and tracking control accuracy. In order to sort out the research status of the key technologies of autonomous navigation for small unmanned vehicles more clearly, this paper firstly reviews the key technologies of autonomous navigation and presents an analysis and summary. Finally, future research trends of small unmanned vehicles with low speed are given.

Assessment of the Technological Maturity of Marine Autonomous Surface Ships

Abstract Marine Autonomous Surface Ships (MASS) are gaining increasing attention due to the potential benefits of improving safety and efficiency. The paper presents the trends in the development of autonomous ships and reliability modeling carried out to assess the maturity of Guidance, Navigation, Control, Power and Propulsion (GNCPP) subsystems that are critical for safe and reliable operation of MASS. Based on the simulations with field-failure data as inputs, it is identified that a typical vessel GNCPP system can have a mean time to fail period of 1.2 years. The emergency shore support systems shall require a proof test interval of 22 days to comply with IEC61508 HSE Safety Integrity Level 3 of on-demand availability. The results could help in efficient maintenance planning and integrity management of the autonomous ship systems. The efforts required to realize situation-aware autonomous navigation technologies complying with stringent operational regulatory frameworks are also discussed.

Autonomous navigation method and technology implementation of high-precision solar spectral velocity measurement

Autonomous navigation method and technology implementation of high-precision solar spectral velocity measurement

Enhanced X-ray pulsar navigation based on ranging information of a satellite

PurposeX-ray pulsar navigation (XPNAV) is an autonomous celestial navigation technology for deep space missions. The error in the pulse time of arrival used in pulsar navigation is large for various practical reasons and thus greatly reduces the navigation accuracy of spacecraft near the Earth and in deep space. This paper aims to propose a novel method based on ranging information that improves the performance of XPNAV.Design/methodology/approachThis method replaces one pulsar observation with a satellite observation. The ranging information is the difference between the absolute distance of the satellite relative to the spacecraft and the estimated distance of the satellite relative to the spacecraft. The proposed method improves the accuracy of XPNAV by combining the ranging information with the observation data of two pulsars.FindingsThe simulation results show that the proposed method greatly improves the XPNAV accuracy by 70% compared with the conventional navigation method that combines the observations of three pulsars. This research also shows that a larger angle between the orbital plane of the satellite and that of the spacecraft provides higher navigation accuracy. In addition, a greater orbital altitude difference implies higher navigation accuracy. The position error and ranging error of the satellite have approximately linear relationships with the navigation accuracy.Originality/valueThe novelty of this study is that the satellite ranging information is integrated into the pulsar navigation by using mathematical geometry.

Particle Filter Design Based on Reinforcement Learning and Its Application to Mobile Robot Localization

Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.

A Loosely Coupled Extended Kalman Filter Algorithm for Agricultural Scene-Based Multi-Sensor Fusion.

With the arrival of aging society and the development of modern agriculture, the use of agricultural robots for large-scale agricultural production activities will become a major trend in the future. Therefore, it is necessary to develop suitable robots and autonomous navigation technology for agricultural production. However, there is still a problem of external noise and other factors causing the failure of the navigation system. To solve this problem, we propose an agricultural scene-based multi-sensor fusion method via a loosely coupled extended Kalman filter algorithm to reduce interference from external environment. Specifically, the proposed method fuses inertial measurement unit (IMU), robot odometer (ODOM), global navigation and positioning system (GPS), and visual inertial odometry (VIO), and uses visualization tools to simulate and analyze the robot trajectory and error. In experiments, we verify the high accuracy and the robustness of the proposed algorithm when sensors fail. The experimental results show that the proposed algorithm has better accuracy and robustness on the agricultural dataset than other algorithms.