Design of Indoor Security Robot based on Robot Operating System

The design and implementation of indoor security robot can well integrate the two fields of indoor navigation and object detection, in order to achieve a more powerful robot system, the development of this project has certain theoretical research significance and practical application value. The project development is completed in ROS (Robot Operating System). The main tools or frameworks used include AMCL (Adaptive Monte Carlo Localization) package, SLAM (Simultaneous Localization and Mapping) algorithm, Darknet deep learning framework, YOLOv3 (You Only Look Once)algorithm, etc. The main development methods include odometer information fusion, coordinate transformation, localization and mapping, path planning, YOLOv3 model training, function package configuration and deployment. Indoor security robot has two main functions: first, it can complete real-time localization, mapping and navigation of indoor environment through sensors such as lidar and camera;Second, object detection is accomplished through USB camera. Through the detailed analysis and research of the functional design of the two modules, the expected function is finally realized, which can meet the daily use needs.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Modulating perovskite crystallization and band alignment using coplanar molecules for high-performance indoor photovoltaics

The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure distortion.Here,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient IPVs.The hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline order.Simultaneously,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier mobility.As a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Impact of the Inlet Flow Angle and Outlet Placement on the Indoor Air Quality

This study aims to optimize the influence of the inlet inclination angle on the Indoor Air Quality(IAQ),heat,and temperature distribution in mixed convection within a two-dimensional square cavityfilled with an air-CO_(2)mixture.The air-CO_(2)mixture enters the cavity through two inlet openings positioned at the top wall,which is set at the ambient temperature(TC).Three values of the Reynolds numbers,ranging from 1000 to 2000,are considered,while the Prandtl number is kept constant(Pr=0.71).The temperature distribution and streamlines are shown for Rayleigh number(Ra)equal to 104,three inlet inclination anglesϕ(0,π/6 andπ/4)and three CO_(2)concentrations values(1500,2500,3500 ppm)applied at both hot vertical walls(maintained at a constant temperature TH).Afinite volume method is used under the assumption of two-dimensional laminarflow to solve the NavierStokes and energy equations.The results indicate that inlet inclination angle has an impact on the indoor air quality(IAQ),which,in turn,affects the heat transfer distribution and thermal comfort within the cavity.

Research and development on mechanism of removal of indoor volatile organic compounds by plants

Background,aim,and scope Owing to the rapid development of modernisation and urbanisation,living standards have gradually improved.However,the widespread use of high-energy-consuming indoor appliances and furniture has made indoor environments a primary environmental problem affecting human health.Sick building syndrome(SBS)and building-related illness(BRI)have occurred,and indoor air conditions have been extensively studied.Common indoor pollutants include CO,CO_(2),volatile organic compounds(VOCs)(such as the formaldehyde and benzene series),NOx(NO and NO_(2)),and polycyclic aromatic hydrocarbons(PAHs).VOCs have replaced SO_(2)as the“The Fourteenth Five-Year Plan”urban air quality assessment new indicators.Indoor VOCs can cause diseases such as cataract,asthma,and lung cancer.To protect human health,researchers have proposed several indoor air purification technologies,including adsorption,filtration,electrostatic dust removal,ozonation,and plant purification.However,each technology has drawbacks,such as high operating costs,high energy consumption,and the generation of secondary waste or toxic substances.Plant degradation of VOCs as a bioremediation technology has the characteristics of low cost,high efficiency,and sustainability,thereby becoming a potential green solution for improving indoor air quality.This study introduces the research status and mechanism of plant removal of indoor VOCs and provides an experimental basis and scientific guidance for analysing the mechanism of plant degradation of pollutants.Materials and methods This study reviews studies on the harm caused by indoor pollutants to human health and related sources,mainly investigating the degradation of indoor formaldehyde,BTEX(benzene,toluene,ethylbenzene,and xylene)plant mechanisms,and research results.Results Plants can remove VOCs via stomatal and non-stomatal adsorption,interfoliar microbial,rhizosphere microbial,and growth media.Benzene,toluene,and xylene(BTX)are adsorbed by pores,hydroxylated into fumaric acid,and then removed into CO_(2) and H_(2)O by TCA.Formaldehyde enters plant leaves through the stomata and epidermal waxy substances and is adsorbed.After the two steps of enzymatic oxidation,formic acid and CO_(2) are generated.Finally,it enters the Calvin cycle and removes glucose and other nontoxic compounds.Discussion The non-stomatal degradation of VOCs can be divided into adsorption by cuticular wax and active adsorption by plant surface microorganisms.The leaf epidermal waxy matter content and the lipid composition of the epidermal membrane covering the plant surface play important roles in the non-stomatal adsorption of indoor air pollutants.The leaf margin of a plant is an ecological environment containing various microbial communities.The endophytic and inoculated microbiota in plant buds and leaves can remove VOCs(formaldehyde and BTEX).Formaldehyde can be directly absorbed by plant leaves and converted into organic acids,sugars,CO_(2) and H_(2)O by microbes.Bioremediation of indoor VOCs is usually inefficient,leading to plant toxicity or residual chemical substance volatilisation through leaves,followed by secondary pollution.Therefore,plants must be inoculated with microorganisms to improve the efficiency of plant degradation of VOCs.However,the effectiveness of interfoliar microbial removal remains largely unknown and several microorganisms are not culturable.Therefore,methods for collecting,identifying,and culturing microorganisms must be developed.As the leaf space is a relatively unstable environment,the degradation of VOCs by rhizosphere microorganisms is equally important,and formaldehyde is absorbed more by rhizosphere microorganisms at night.The inoculation of bacteria into the rhizosphere improves the efficiency of plants in degrading VOCs.However,most of these studies were conducted in simulation chambers.To ensure the authenticity of these conclusions,the ability of plants to remove indoor air pollutants must be further verified in real situations.Conclusions Plant purification is an economical,environment-friendly,and sustainable remediation technology.This review summarises the mechanisms of VOC plant degradation and presents its limitations.Simultaneously,it briefly puts forward a plant selection scheme according to different temperatures,light,and specific VOCs that can be absorbed to choose the appropriate plant species.However,some studies have denied the purification effect of plants and proposed that numerous plants are required to achieve indoor ventilation effects.Therefore,determining the ability of plants to remove indoor VOCs requires a combination of realistic and simulated scenarios.Recommendations and perspectives Plants and related microorganisms play an important role in improving indoor air quality,therefore,the effect of plants and the related microorganisms on improving indoor air quality must be studied further and the effect of plants on indoor VOCs will be the focus of future research.

Suppressing Leakage Currents and Improving Performance of Indoor Organic Photovoltaic Devices

Organic photovoltaic(OPV) devices hold great promise for indoor light harvesting,offering a theoretical upper limit of power conversion efficiency that surpasses that of other photovoltaic technologies.However,the presence of high leakage currents in OPV devices commonly constrains their effective performance under indoor conditions.In this study,we identified that the origin of the high leakage currents in OPV devices lay in pinhole defects present within the active layer(AL).By integrating an automated spin-coating strategy with sequential deposition processes,we achieved the compactness of the AL and minimized the occurrence of pinhole defects therein.Experimental findings demonstrated that with an increase in the number of deposition cycles,the density of pinhole defects in the AL underwent a marked reduction.Consequently,the leakage current experienced a substantial decrease by several orders of magnitude which achieved through well-calibrated AL deposition procedures.This enabled a twofold enhancement in the power conversion efficiency(PCE) of the OPV devices under conditions of indoor illumination.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter

Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.

Comprehensive Analysis of Indoor Formaldehyde Removal Techniques:Exploring Physical,Chemical,and Biological Methods

This research focuses on the evaluation of diverse approaches for removing formaldehyde from indoor environments,which is a significant concern for indoor air quality.The study systematically examines physical,chemical,and biological methods to ascertain their effectiveness in formaldehyde mitigation.Physical methods,including air circulation and adsorption,particularly with activated carbon and molecular sieves,are assessed for their efficiency in various concentration scenarios.Chemical methods,such as photocatalytic oxidation using titanium dioxide and plasma technology,are analyzed for their ability to decompose formaldehyde into non-toxic substances.Additionally,biological methods involving plant purification and microbial transformation are explored for their eco-friendly and sustainable removal capabilities.The paper concludes that while each method has its merits,a combined approach may offer the most effective solution for reducing indoor formaldehyde levels.The study underscores the need for further research to integrate these methods in a practical,cost-effective,and environmentally sustainable manner,highlighting their potential to improve indoor air quality significantly.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Multilayer ANN indoor location system with area division in WLAN environment

An indoor location system based on multilayer artificial neural network（ANN） with area division is proposed.The characteristics of recorded signal strength（RSS）,or signal to noise ratio（SNR） from each available access points（APs）,are utilized to establish the radio map in the off-line phase.And in the on-line phase,the two or three dimensional coordinates of mobile terminals（MTs） are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map.Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points,the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment.Then,the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented.And also,the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division,K-nearest neighbor（KNN） and probability methods in typical office environment.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.

Position Vectors Based Efcient Indoor Positioning System

With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.

Novel indoor positioning system based on ultra-wide bandwidth

To tackle challenges such as interference and poor accuracy of indoor positioning systems,a novel scheme based on ultra-wide bandwidth(UWB)technology is proposed.First,we illustrate a distance measuring method between two UWB devices.Then,a Taylor series expansion algorithm is developed to detect coordinates of the mobile node using the location of anchor nodes and the distance between them.Simulation results show that the observation error under our strategy is within 15 cm,which is superior to existing algorithms.The final experimental data in the hardware system mainly composed of STM32 and DW1000 also confirms the performance of the proposed scheme.

RSS-Based Indoor Localization System with Single Base Station

The paper proposes an Indoor Localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable antennas.The proposed algorithm measures Received Signal Strength(RSS)and maps it to the location in the room by estimating signal strength of a direct line of sight(LOS)signal and signal of the first order reflection from the wall.The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office,sampling 21 different locations in the room.It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80%confidence Cumulative Distribution Function(CDF)user level,demonstrating the ability to accurately estimate the receiver’s location within the room.The system is intended as a cost-efficient indoor localization technique,offering simplicity and easy integration with existing wireless communication systems.Unlike comparable single base station localization techniques,the proposed system does not require beam scanning,offering stable communication capacity while performing the localization process.