Autonomous Localization Research Articles

Design of Smart Home Service Robot Based on ROS

At present, the functions of home service robots are not perfect, and home service robot systems that can independently complete autonomous inspections and home services are still lacking. In response to this problem, this paper designs a smart home service robot system based on ROS. The system uses Raspberry Pi 3B as the main control to manage the nodes of each sensor. CC2530 sets up a ZigBee network to collect home environmental information and control home electrical appliances. The image information of the home is collected by the USB camera. The human speech is recognized by Baidu Speech Recognition API. When encountering a dangerous situation, the GSM module is used to give users SMS and phone alarms. Arduino mega2560 is used as the bottom controller to control the movement of the service robot. The indoor environment map of the home is constructed by the lidar and the attitude sensor. The service robot finally designed and developed realizes the functions of wireless control of home appliances, voice remote control, autonomous positioning and navigation, liquefied gas leakage alarm, and human infrared detection alarm. Compared with the household service robots in the related literature, the household service robots developed by us have more complete functions. And the robot system has completed the task of combining independent patrol and home service well.

Routing protocols & unmanned aerial vehicles autonomous localization in flying networks

SummaryNowadays, as the world is transformed because of the interconnectivity, there we see several issues regarding security which is supposed to be one of the biggest problems for the concept of smart cities. In future, aerial vehicles can be merged with smart cities to enhance the interoperability among internet of things (IoT) devices. Routing protocols plays an important role when flying vehicles communicate with one another. Reinforcing connection in flying ad hoc networks using routing protocols is the optimal way to reduce end‐to‐end delay. However, utilizing flying networks with the deployment of routing techniques is a very difficult task to tackle the dynamic pattern of aerial vehicles. Internet of flying vehicles is the emerging area which is still in the evolutionary process; however, the interconnectivity can be efficiently formulated by applying security algorithms for improving signal power. In unmanned aerial vehicles (UAVs), every node will have some specific location, direction, speed, velocity, and acceleration which will identify and relate information about mobility model. This paper proposes a novel routing technique, which is the extension of AntHocNet due to mobile characteristics; it is termed as flying nature inspired algorithm. Also a case study is conducted to improve the signal power using modeled learning approach called decision tree. Experimental simulations are managed on network simulators that prove the proposed work efficiency.

Performance Analysis of GNSS Receiver allied in High-Orbit Based on SoC Platform

GNSS receiver technologies allied in high orbit altitudes has developed rapidly in recent years and has been used in orbit on many spacecraft. In this paper, TJS-5 Satellite is taken as an example to illustrate the design scheme of the high orbit navigation receiver based on the SoC platform, which takes the characteristics of the weak signals of GEO orbit navigation into consideration, and adopts the high sensitivity acquisition technology and the fast signal acquisition technology. Finally, the 19-day in-orbit data of TJS-5 Satellite are analyzed. The number of available satellites is between 4 and 8, the position accuracy of orbit determination is 15.8133m and the velocity is 0.012108m/s, which meets the needs of autonomous positioning and precise orbit determination in the whole orbit period of the satellite. The calculated results are compared with those of TJS-2 satellite.

Research on Visual Positioning of a Roadheader and Construction of an Environment Map

The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.

An EKF-based multiple data fusion for mobile robot indoor localization

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

Path Planning for Localization of Radiation Sources Based on Principal Component Analysis

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.

Ground beetle (Coleoptera, Carabidae) communities as natural indicators of ecosystem restoration on the terminated fly ash dump of the thermoelectric power station (Novosibirsk, Russia)

The aim of the study. The aim of the study was to reveal regularities in the development of ground beetles (Coleoptera, Carabidae)’ communities, to determine their species peculiarities as dependent on soil formation rate and soil ecology in the ecosystems developed on the terminated fly ash dump of the thermoelectric power station over nine years of restoration. Location and time of the study. The main object of the study was a terminated fly ash dump No.1 of the thermoelectric power station No.5 in Novosibirsk, Russia. (54°59' NL, , 83°03' EL). Soil samples were collected in 2019, whereas the ground beetles were collected in 2017 and 2019. Methodology. Six study sites, corresponding to the fly ash dump sedimentation zones, were chosen. The undisturbed white birch forest and clover-brome meadow were chosen as controls representing mature climax ecosystems. Soil cover, developing on the fly ash dump after its termination, was described according to the soil classification of technogenic landscapes, and was shown to consist of various types of embryozems (Technosols). Soil water content on each study site was measured in 0–5cm layer by gravimetric method. Soil samples were collected in May 2019. To collect ground beetles five traps were placed on each study site, each trap filled up to 1/3 of its volume with 4% acetic acid for fixing the trapped beetles. The beetles were collected in May–June 1017 and in June–July 2019. The data obtained were used to calculate α-biodiversity indices and perform cluster analysis using PAST v.3.09 software. Main results. Altogether 74 species of the ground beetle we found: 60 species of ground beetles were identified in the revegetating fly ash dump areas, with 48 species found at the sites amended with potentially fertile substrate (PFS), and 35 species found at the sites without PFS addition. The control sites under birch forest and meadow had 14 and 10 species, respectively. Thus the ground beetles’ abundance in the reclaimed area was almost 3 times greater than in the non-reclaimed area (973 specimens vs. 347). Cluster analysis discriminated three groups in the studied fly ash dump sites, namely dry non-reclaimed sites, dry PFS sites and moist sites. Soil cover of the studied fly ash dump was represented by initial, organic matter accumulating, humus-accumulating and sod embryozems (Technosols). Conclusion. Over nine years of spontaneous restoration at the terminated fly ash dump of the thermoelectric power station species rich and abundant communities of ground beetles were formed. These communities differed significantly from those at the adjacent undisturbed climax ecosystems used for comparison. Species richness and dynamic density of ground beetles was shown to increase from the control sites to non-reclaimed sites and PFS-reclaimed sites. The autonomous position in the catena was found to be occupied by the less evolutionary developed embryozem type, whereas downwards along the catena, i.e. with increasing soil moisture content, identified soil types belonged to higher evolutionary order. Therefore technical recultivation with fly ash dump overlaying with potentially fertile substrate or fertile soil allow accelerating soil cover development, as well as the development of an entire biogeocenosis.

Research on Multi-AGV Management System of Autonomous Navigation AGVs for Manufacturing Environment

This paper serves intelligent AGVs which can autonomous positioning, autonomous navigation and autonomous movement. And build a multi-AGV management system for manufacturing environment applications, which is relatively complete functions and good interaction between users and AGVs. In order to meet the requirements, the system development process highlights the functional design of the AGV management system and optimizes the data processing capabilities. Complete the construction of the interface and database on the Qt and My SQL software. The test was conducted after importing into the test database. The results showed that the built system operated smoothly and the data processing was correct and timely.

Application of autonomous navigation in robotics

In recent years, the robot industry has made great progress, and has been widely used in automobile manufacturing, machining, transportation agriculture, medical treatment, food and other aspects. The autonomous navigation is a key technology in the development of robot intelligence. Therefore, this paper extensively investigates the applications of autonomous navigation technology in the transfer robot, the medical robot and the agricultural robot. And we analyze and summarize the developments and applications of autonomous navigation related basic technologies, such as autonomous positioning technology, sports planning technology and 5 g communication technology. Finally, the future development prospect of robot technology and the autonomous navigation technology is given.

National modernisation and global science in China

The paper reviews the rapid growth and improvement of science in China since Deng Xiaoping’s reforms in the 1980s and especially in the last two decades, situating the achievement and its limitations in the contexts of both the global science system, and national policy and strategy. The key is the effective combination of national science with global science. Science in China has worked to global norms while remaining nested in national government and Sinic governance, in which Confucianism and Legalism are combined with Leninism. The paper reflects on trends in investment in R&D, the number of published papers, discipline balance, national and global networking, and policies on cross-border partnerships and mobility. China has established a strong autonomous position at world level in science that is partly separated from the intensively networked Euro-American systems but has benefitted especially from a mutually productive relationship with the United States. However, that relationship is now in question.

A UAV Autonomous Positioning Algorithm Based on Satellite Image Matching

A UAV Autonomous Positioning Algorithm Based on Satellite Image Matching

Moving to new generational beats: Lived experiences of capitalism, student-led (re)makings of knowledge, and the evolution of critical research agendas

Via a reflection on the evolution of a module on comparing capitalisms that I have been teaching for more than a decade, this article discusses the collective influence of new generations of students on how knowledge is (re)made. I deploy a conjunctural understanding of the term ‘generations’ in order to make sense of how students’ interpretations of the topics covered by the module have, across the 2010s, led me to increasingly question the field that was, in an earlier conjuncture, essential for my intellectual foundation and development. Their lived experiences of capitalism are more likely to be dominated by themes such as political, economic and social crises and conflicts, inequality, personal indebtedness and precarity, and in some cases activism. This has had profound and long-lasting effects on my teaching and research, discomfiting me in an ultimately beneficial way; most notably, through the recognition that future critical work on comparing capitalisms ought to move away from previous attempts to engage immanently with dominant, mainstream approaches and towards the articulation of a more confident, autonomous position. Hence, a key aspect of the development and evolution of critical research agendas occurs in and through educational exchanges in the seminar room.

Overview of Obstacle Avoidance Algorithms for UAV Environment Awareness

In complex indoor and outdoor environment, obstacle avoidance of UAV (Unmanned Aerial Vehicles) is a challenging problem. In order to realize the obstacle detection, autonomous positioning and trajectory planning of UAV flight mission in large outdoor scene, there are three main technical problems: Firstly, UAV needs to have the ability to quickly detect a variety of obstacles in outdoor scene. Secondly, in order to realize the autonomous navigation of UAV, it needs to establish the coordinates of obstacles in three-dimensional space. Thirdly, based on the above two conditions, UAV can independently plan flight path to avoid obstacles. This paper mainly introduces the use of RGB-D camera, lidar, monocular camera and binocular camera in UAV obstacle avoidance, and compares them from the sensor types, advantages disadvantages and application range. Secondly, the path planning strategy of UAV is discussed, and the existing problems and current research results of UAV trajectory planning strategy are described. Finally, it is pointed out that real-time computing, multi-sensor fusion and integrated obstacle avoidance between multi aircraft should be the research direction of autonomous obstacle avoidance navigation for UAV in the future.

Mobile Manipulator for Autonomous Localization, Grasping and Precise Placement of Construction Material in a Semi-Structured Environment

Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditions alike. Finally, we present means to extend the perceptive radius of the vehicle by using it in cooperation with an autonomous drone, which provides aerial reconnaissance. Performance of the proposed system has been evaluated in a series of experiments conducted in real-world conditions.

A Graphical-User-Interface-Based Azimuth-Collection Method in Autonomous Auditory Localization of Real and Virtual Sound Sources.

Auditory localization of spatial sound sources is an important life skill for human beings. For the practical application-oriented measurement of auditory localization ability, the preference is a compromise among (i) data accuracy, (ii) the maneuverability of collecting directions, and (iii) the cost of hardware and software. The graphical user interface (GUI)-based sound-localization experimental platform proposed here (i) is cheap, (ii) can be operated autonomously by the listener, (iii) can store results online, and (iv) supports real or virtual sound sources. To evaluate the accuracy of this method, by using 12 loudspeakers arranged in equal azimuthal intervals of 30° in the horizontal plane, three groups of azimuthal localization experiments are conducted in the horizontal plane with subjects with normal hearing. In these experiments, the azimuths are reported using (i) an assistant, (ii) a motion tracker, or (iii) the newly designed GUI-based method. All three groups of results show that the localization errors are mostly within 5-12°, which is consistent with previous results from different localization experiments. Finally, the stimulus of virtual sound sources is integrated into the GUI-based experimental platform. The results with the virtual sources suggest that using individualized head-related transfer functions can achieve better performance in spatial sound source localization, which is consistent with previous conclusions and further validates the reliability of this experimental platform.

Funding for few, anticipation among all: Effects of excellence funding on academic research groups

AbstractIn spite of the growing literature about excellence funding in science, we know relatively little about its implications for academic research practices. This article compares organizational and epistemic effects of excellence funding across four disciplinary fields, based on in-depth case studies of four research groups in combination with twelve reference groups. In spite of the highly selective nature of excellence funding, all groups employ dedicated strategies to maximize their chances of acquiring it, which we call strategic anticipation. The groups with ample excellence funding acquire a relatively autonomous position within their organization. While the epistemic characteristics of the four fields shape how excellence funding can be used, we find that in all fields there is an increase in epistemic autonomy. However, in fields with more individual research practices a longer time horizon for grants, beyond the usual 5 years, would fit better with the research process.

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.

Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

FracBot Technology for Mapping Hydraulic Fractures

Summary Miniaturized transponder systems are under development for monitoring unconventional reservoirs, mapping hydraulic fractures, and determining other wellbore parameters. These gadgets are an extension of radio-frequency identification (RFID) and are known as fracture robot (FracBot) nodes to recognize wireless underground sensor networks (WUSNs) for characterization and mapping of hydraulic breakages in unconventional reservoirs. 3D constellation maps of proppant-bed placement are generated by autonomous localization algorithms as FracBots are injected during hydraulic-fracturing operations. To investigate this model, a FracBot platform was established to explore this concept, and three basic functions have been explained. First, we have developed an innovative cross-layer communication model for magnetic-induction (MI) networks in altering underground environments, coupled with selections of coding, modulation, and power control and a geographic forwarding structure. Second, we have developed an innovative MI-based localization framework to capture the locations of the randomly deployed FracBot nodes by exploiting the exceptional properties of the MI field. Third, we have proposed an energy model for a linear FracBot network scheme that provides reasonable data rates while preserving collected energy limitations. Finally, to examine the functionalities of FracBot nodes in air, sand, and stone media, a physical MI-based WUSN test bed was implemented. Experiments indicated that the constructed FracBots can form a communication link and transfer data over amplitude-shift keying (ASK) modulation with 1.6 kbit/sec as a data rate and a minimum receiver sensitivity of −70 dBm. The performance of near-field-communication (NFC) antennas was affected by sand and stone media, which ultimately affect MI signal propagation and decrease the energy transfer. In sand or stone media, augmented mismatch between transmitter and receiver antennas was detected, leading to the decision that an advanced matching circuit design or an adaptive-frequency feature should be integrated into the FracBot design. This permits an optimal energy transmission and consistent communication link through sand and stone media.

Bionic Integrated Positioning Mechanism Based on Bioinspired Polarization Compass and Inertial Navigation System.

In this paper, to address the problem of positioning accumulative errors of the inertial navigation system (INS), a bionic autonomous positioning mechanism integrating INS with a bioinspired polarization compass is proposed. In addition, the bioinspired positioning system hardware and the integration model are also presented. Concerned with the technical issue of the accuracy and environmental adaptability of the integrated positioning system, the sun elevation calculating method based on the degree of polarization (DoP) and direction of polarization (E-vector) is presented. Moreover, to compensate for the latitude and longitude errors of INS, the bioinspired positioning system model combining the polarization compass and INS is established. Finally, the positioning performance of the proposed bioinspired positioning system model was validated via outdoor experiments. The results indicate that the proposed system can compensate for the position errors of INS with satisfactory performance.