Window Algorithm Research Articles

OPENING DESIGN FOR DOUBLE-LAYER CONCRETE BOX GIRDER BASED ON TOPOLOGY OPTIMIZATION

The double-deck concrete box girder is composed of two kinds of material, steel and concrete, and their working properties can be represented by a tie rod-compression rod model composed of steel and concrete. A new topology optimization method called progressive structure optimization is introduced to establish the tie rod-compression rod model reflecting the working mechanism of the structure, which provides theoretical guidance for simplifying the opening design of the double-layer concrete box girder web. This paper introduces the optimal topology solution to establish the tie rod-compression rod model and how to complete the structural reinforcement design. The result shows that the topological optimal solution obtained by the windowed progressive structure optimization algorithm can better represent the internal stress state of the structure; and by the optimization method, the opening ratio of I-beam structure is increased by 5.5 times compared with the experimental design, the ultimate load is increased by 1.2 times, but the amount of steel is reduced by 30%.

Hierarchical planning for multiple AGVs in warehouse based on global vision

In automated warehouses, multiple automated guided vehicles (AGVs) are widely used to sort parcels for improving storage efficiency. However, as the number of AGVs increases, the scheduling complexity also increases dramatically. This paper proposes a hierarchical planning algorithm for efficiently coordinating a fleet of AGVs in warehouses based on global vision. We use the camera installed on the top of the warehouse to capture the global image that is used to track AGVs. Based on a network congestion diffusion model, we quantify the road congestion and add it to the evaluation index to form a time-varying dynamic evaluation function. Then the improved A* algorithm and time window algorithm are combined as the hierarchical planning algorithm to search the idle path and avoid collisions. Extensive simulations show that the hierarchical planning based on global vision not only effectively schedules multiple AGVs in the warehouse, but also has lower time complexity than other real-time path planning algorithms.

Urban Heat Island Monitoring and Impacts on Citizen's General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach.

Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 ± 2.3 °C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens’ health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education.

Retrieval of precise land surface temperature from ASTER night-time thermal infrared data by split window algorithm for improved coal fire detection in Jharia Coalfield, India

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST.

Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons

With the rapid advancement of the high-performance computing technology and the increasing availability of the mass-storage memory device, the application of the data-driven models (e.g., the artificial neural network (ANN) model) for solar radiation prediction is appearing in abundance in the past decade. Although the performances of these models have been discussed in a large number of studies, how to further enhance the forecasting accuracies of these data-driven approaches to better facilitate the advanced controls in the building system such as model predictive control (MPC) in smart buildings remains a challenge. Deep learning, which is considered as a powerful tool to move machine learning closer to one of its original goals, i.e., Artificial Intelligence (AI), is a viable solution to this problem.In this study, an ANN model and a recurrent neural network (RNN) model are developed to investigate the performances of the deep learning algorithms for the solar radiation prediction. The actual meteorological data (AMY) from a local weather station in Alabama is used for the training process. Various scenarios, including different sampling frequencies and moving window algorithms, are included for a comprehensive evaluation of the accuracies and efficiencies. The results suggest that compared with the ANN model, the solar radiation prediction using the RNN model has a higher prediction accuracy, with a 47% improvement in Normalized Mean Bias Error (NMBE) and a 26% improvement in Root-Mean-Squared Error (RMSE). Besides, this forecasting accuracy could even be taken to a higher level by increasing the granularity of the data or adding a moving-window algorithm to the prediction model. By increasing the sampling frequency of the training data from 1 h to 10 min, the Root-Coefficient of Variation Mean-Squared Error (CV(RMSE)) of the ANN model dropped from 30.9% to 9.41%, while the CV(RMSE) of the RNN model dropped from 9.83% to 7.64%. For the RNN model, the NMBE was improved from 0.9% to 0.2% after the implementation of the moving-window algorithm. Besides, it was found that the cloud cover could have a significant impact on the prediction accuracy.

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

A new quality evaluation parameter for Rayleigh backscattering spectrum and its adaptive subset window algorithm in distributed fiber strain measurement

• A quality factor for the Rayleigh scattering spectrum is defined. • An algorithm of the adaptive subset window length is proposed. Rayleigh backscattering spectrum (RBS) correlation based distributed fiber strain measurement can be achieved by calculating the RBS offset using cross-correlation function from an unstrained and strained optical fiber under test. Thus the subset window length and the quality of the RBS in the subset have an important impact on the accuracy of the cross-correlation analysis and the strain measurement. How to evaluate the quality of different RBS signals plays an important role in optimizing the subset window length and improving the use of the technique. In this paper, a parameter called mean of the square of intensity gradient (MSIG) is proposed for quality assessment of the RBS signals. Further, an adaptive subset window algorithm base on the MSIG of each subset is developed for the subset window selection. The experiments verify the effectiveness and accuracy of the proposed parameter. The results show that the RBS with larger MSIG has smaller standard deviation error and higher signal quality. The adaptive subset window algorithm based on MSIG proposed in this paper can effectively improve the strain calculation accuracy of the distributed fiber strain measurement.

A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

The modeling of Near-Surface Temperature Lapse Rate (NSTLR) is of great importance in various environmental applications. This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature (NLST). A set of remote sensing imagery including Landsat images, MODIS products, and ASTER Digital Elevation Model (DEM), land cover maps, and climatic data recorded in meteorological stations and self-deployed devices located in the three study area were used for modeling and evaluation of NSTLR. First, the Split Window (SW) and Single Channel (SC) algorithms were used to estimate LST, and the spectral indices were used to model surface biophysical characteristics. The solar local incident angle was obtained based on topographic and time conditions for different dates. In the second step, the NSTLR value was calculated based on the LST-DEM feature space at the regional scale. The LST was normalized relative to the surface characteristics based on Random Forest (RF) regression and the NSTLR was calculated based on the NLST-DEM feature space. Finally, the coefficient of determination (R2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between DEM and LST for the study area A, B and C, respectively. The R2 and RMSE between the observed NSTLR and the Landsat derived NSTLR based on NLST for the study area A (B, C) were improved 0.30 (0.26, 0.35) and 0.81 (0.80, 0.94) °C Km−1, respectively, rather than the Landsat derived NSTLR based on LST. Also, for the study area A, the R2 and RMSE between the observed NSTLR and the MODIS derived NSTLR based on NLST in spring, summer, autumn and winter were improved 0.17, 0.12, 0.10, and 0.22; and 0.51, 0.44, 0.27, and 0.51 °C Km−1, respectively, rather than the MODIS derived NSTLR based on LST. Model assessment results (R2 and RMSE) and comparing modeled NSTLR (all strategies) with observed NSTLR, for both Landsat and MODIS, showed that the use of NLST instead of LST, significantly improved the accuracy of the obtained NSTLR in the mountainous regions.

Multi-focus image fusion for multiple images using adaptable size windows and parallel programming

The multi-focus image fusion with adaptable windows (MF-AW) algorithm for multiple images improves the results of the linear combination of images with variable windows (CLI-VV—from its Spanish acronym) algorithm, using a unique decision map and applying parallel programming. Other algorithms use the same window size throughout the image to produce a decision map; furthermore, a different decision map is produced for each pair of images. MF-AW determines the largest possible window size delimited by the edges of the decision map, which are improved using an iterative process. The execution time is improved using integral images, binary search, and parallel programming; as a result, the fused image is obtained in tenths of a second. Quantitative and qualitative measures indicate that the results obtained with this algorithm outperform the state of the art in terms of both accuracy and execution time.

Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data

Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 °C higher than the surrounding water and exceeds 6 °C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide.

RedCap: residual encoder-decoder capsule network for holographic image reconstruction.

A capsule network, as an advanced technique in deep learning, is designed to overcome information loss in the pooling operation and internal data representation of a convolutional neural network (CNN). It has shown promising results in several applications, such as digit recognition and image segmentation. In this work, we investigate for the first time the use of capsule network in digital holographic reconstruction. The proposed residual encoder-decoder capsule network, which we call RedCap, uses a novel windowed spatial dynamic routing algorithm and residual capsule block, which extends the idea of a residual block. Compared with the CNN-based neural network, RedCap exhibits much better experimental results in digital holographic reconstruction, while having a dramatic 75% reduction in the number of parameters. It indicates that RedCap is more efficient in the way it processes data and requires a much less memory storage for the learned model, which therefore makes it possible to be applied to some challenging situations with limited computational resources, such as portable devices.

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

Combination of visible and thermal remotely sensed data for enhancement of Land Cover Classification by using satellite imagery

On a large scale, the land cover classification has been investigated throughout the world in remote sensing for different kinds of applications such as water resources, agricultural, environmental, as well as ecological and hydrological applications. In order enhance accuracy of the classification results, Landsat and multispectral bands are used to study the numerous classification methods. Remote sensing thermal data provides valuable information in order to examine the effectiveness of applying the thermal bands to extract useful land cover thematic maps. In this research, Landsat-8 satellite data captured by Operational Land Imager (OLI) and the Thermal Infrared (TIRS) Sensors, with using remotely sensing data and Geographic Information System (GIS) analysis with using ground truth data collect from fieldwork in same time of imagery capturing by using infrared thermometer camera. In 2018, single date Landsat-8 image of the study area in Iraq was captured in winter. This image is used to estimate Land Surface Temperature (LST) by split window algorithm and performing Land Cover (LC) classification after image noise removal by using supervised classification algorithms Support Vector Machine (SVM) with multi-spectral and thermal bands combinations to find out which one has more accuracy. Result shows the effective and efficiency of the proposed method compared by traditional classification methods. The overall accuracy and Kappa coefficient are 94.25%, 64.43% and 0.93, 0.63, respectively.

Integrating adaptive moving window and just-in-time learning paradigms for soft-sensor design

Most applications of soft sensors in process industries require learning from a stream of data, which may exhibit nonstationary dynamics, or concept drift. In this study, we develop a relevance vector machine (RVM) based novel adaptive learning algorithm called MWAdp-JITL, to meet the demands of continuous processes. The resulting algorithm combines active and passive learning: A moving window (MW) algorithm, which adapts the window size against virtual/real concept drifts, is coupled with a just-in-time learning (JITL) model, constructed using an appropriate region of historical data, and the ensemble weights of the MW and JITL models are adjusted for each query point. Tests on four real industrial datasets and a synthetic data, comprising various concept drift scenarios, show that MWAdp-JITL yields superior prediction accuracy and is generally more robust to changes in algorithm parameters compared to conventional adaptive learning methods and state-of-the-art algorithms from the literature. MWAdp-JITL complies with time limits of online prediction, and is applicable for high dimensional processes under various types of concept drifts. It is seen that MWAdp-JITL can successfully achieve a good balance in bias-variance tradeoff, justifying the use of only two exquisitely selected learners in ensemble learning.

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

Research on flame location based on adaptive window and weight stereo matching algorithm

Flame location based on binocular vision has some problems such as low matching accuracy, slow matching speed and so on. To solve the above problems, we propose stereo edge matching algorithm based on adaptive window and weight. Since image edge regions has rich information, an edge based adaptive window algorithm is proposed. For non-edge regions, adaptive weight algorithm based on row and column is accumulated separately, which is used to reduce computational complexity. Finally it gets the position of the flame according to disparity map. The experimental results show that the matching algorithm based on adaptive window and weight can locate the fire source. Its error is within 10 cm and the matching speed is higher and the effect is better.

An efficient algorithm for reducing the flow of real-time data stream with least sampling error

Nature of data stream is determined after complete scanning of whole data sets during real-time data processing. However, it becomes inconvenient to process entire data stream at once in real-time data stream processing. Thus, a sheer sized fixed window of data streams is processed at a particular time. The intensification of sheer sized fixed window at processing node is mitigated by reducing the flowing rate of data stream. Heuristic clustering windowing (HCW) approach and partial blind window (PBW) algorithms are proposed for reducing the flow of data stream with least sampling error. These approaches consist of the combination of systematic sampling and clustering mechanism. A clustering approach is applied on one fraction of data streams whereas systematic sampling handles other portion of streams. These approaches are helpful in reducing flow of data streams in minimum latency.

A Robust Autonomous Following Method for Mobile Robots in Dynamic Environments

Autonomous following is one of the key issues in mobile robotics with a wide range of applications in commercial, industrial and military areas. To achieve autonomous following, the robot needs not only to robustly detect and track the target, but also efficiently follow the target while avoiding obstacles. In this paper, we propose a method that combines a Line-of-sight sensor (a 2D laser range finder) and a Non-line-of-sight sensor (a low-cost Angle-of-Arrival (AOA) tag) to identify, track and follow the target person in dynamic environments. First, in order to get smooth and continuous moving trajectory of the target person, a Kalman Filter is used to fuse person tracking information from AOA data and laser data. Then, a real-time robot-centric rolling grid map is constructed using the laser data. On top of the rolling grid map, a target potential field is generated by using the fast marching method, and then a direction gradient field is created based on the target potential field. With the rolling grid map, the target potential field and the direction gradient field, an improved dynamic window algorithm, FMM-DWA, is proposed to control the robot to move towards the target. The proposed method considers not only the distance between the robot and the target, but also the difference between the heading of the robot and the reference direction provided by the direction gradient field, to avoid falling into the local optimum. To validate the performance of the proposed method, a series of person following experiments are carried out in complex dynamic environments. The experimental results show that the proposed following algorithm can effectively deal with the occlusion problem and robustly follow the target person while quickly avoiding the static and dynamic obstacles.