Sensor Planning Research Articles

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

Automated CAD-based sensor planning and system implementation for assembly supervision

Automated CAD-based sensor planning and system implementation for assembly supervision

OCAGraph: An effective observation capability association model for Earth observation sensor planning

• Association relations among Earth observation (EO) sensors are modeled as a graph. • N -ary association relations among EO sensors are characterized for the first time. • Time-varying association relations are captured by multiple space–time snapshots. • Collaborative observation planning can be achieved comprehensively and optimally. The optimal collaborative observation planning of Earth observation (EO) sensors is essential for disaster monitoring. This goal requires comprehensively characterizing the observation capability associations among these sensors. Previous studies only solved the static binary-associated observation capability for the optimized collaborative planning of two-sensor combinations, while the dynamic N -ary-associated observation capability that supports the optimized collaborative planning of N -sensor combination remained to be addressed. This study proposes a space–time composite observation capability association graph (OCAG) model, where its constituent sensors and association relations are modeled as nodes and edges, respectively, to address this gap. The proposed model divides an observation scenario into several states, with each state utilizing an OCAG to characterize the association relations, especially the N -ary association relations, among the EO sensors. The N -ary association relations can function as optimized observation plans to satisfy various observation requirements. A flood observation planning experiment was conducted in Hubei, China. Experimental results indicate that all association relations, especially the N -ary ones, can be solved at multiple space–time snapshots, enabling sensor planners to comprehensively and optimally collaborate EO sensors for flood observation.

A sampling-based motion planning method for active visual measurement with an industrial robot

• A new framework for active visual measurement with an industrial robot is proposed. • The proposed framework considers a lot of constraints on the robot and camera's field of view during the measurement. • The proposed framework performs high efficiency and excellent performance for sensor planning. This paper proposes a universal and complete framework for active visual measurement with an industrial robot based on sampling-based motion planning techniques with high efficiency and excellent performance. The proposed path planner is based on a tree-based randomized planning scheme to generate a point-to-point path satisfying the camera's field of view constraints, the occlusion-free and collision-free constraints in the joint space. The speed planner first smoothes the path based on the B-spline curve and then carries out the time-optimal speed planning satisfying the joint velocity, acceleration, jerk constraints, and the camera's velocity constraints. The proposed method in this paper focuses on improving the motion performance of the visual measurement. The satisfaction of the joint jerk constraints and the camera's velocity constraints enables the robot to run smoothly along the generated trajectory, reducing the vibration of the camera fixed on the end effector significantly. Therefore, the pictures taken by the camera at a certain frequency can all be used for the measured workpiece's point cloud reconstruction, and the experiments verified the feasibility and effectiveness of our proposed framework.

Automatic path planning for high performance measurement by laser plane sensors

The Fourth Industrial Revolution (4IR) has led to a wide development of laser sensors and their use in inspection processes to achieve high performance is becoming more and more common. High measurement performance implies high measurement quality of surfaces in a short time. Generally, the quality is evaluated on the basis of indicators such as noise and trueness. Recently, it has been found that these indicators are mostly affected by the scanning strategy defined by two main parameters: the scanning angle and distance. Indeed, the path planning of laser sensors ensuring a high level of quality is based on the definition of the scanning strategy allowing scanning well-known surfaces with reduced noise and trueness. In this paper, an original approach for the generation of automatic path planning is proposed. The originality lies in the high performance obtained by optimizing the quality and the measurement time based on the optimal path planning. First, the laser sensor is qualified in order to evaluate the quality indicators as a function of the scanning distance and angle. Configurations that provide better quality are determined. By analyzing all laser sensor orientations held by the CMM's rotating head, a set of minimum orientations allowing high quality measurement is determined based on an optimization algorithm regarding quality, accessibility, visibility and collision detection criteria. The laser sensor path is based on the determination of the path levels in each sensor orientation. An original method is proposed to generate the optimal levels guaranteeing a high quality in terms of scanning distance. The laser sensor crossing points are then calculated in each level. An improvement of the method is proposed through the connection of levels using connection lines. Finally, collisions are studied and fixed by adding translation points. The proposed approach is applied to a part defined by its CAD model. The path is generated and executed on the CMM with a laser sensor. The measurement performance is compared to previous approaches in the literature. The methodology allows an adapted planning and can be used for automated dimensional inspection.

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges.

Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

Active object tracking using context estimation: handling occlusions and detecting missing targets

When performing visual servoing or object tracking tasks, active sensor planning is essential to keep targets in sight or to relocate them when missing. In particular, when dealing with a known target missing from the sensor’s field of view, we propose using prior knowledge related to contextual information to estimate its possible location. To this end, this study proposes a Dynamic Bayesian Network that uses contextual information to effectively search for targets. Monte Carlo particle filtering is employed to approximate the posterior probability of the target’s state, from which uncertainty is defined. We define the robot’s utility function via information theoretic formalism as seeking the optimal action which reduces uncertainty of a task, prompting robot agents to investigate the location where the target most likely might exist. Using a context state model, we design the agent’s high-level decision framework using a Partially-Observable Markov Decision Process. Based on the estimated belief state of the context via sequential observations, the robot’s navigation actions are determined to conduct exploratory and detection tasks. By using this multi-modal context model, our agent can effectively handle basic dynamic events, such as obstruction of targets or their absence from the field of view. We implement and demonstrate these capabilities on a mobile robot in real-time.

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

AbstractPropagation modeling of small/big pebbles and air-dry/wet sand environments for wireless sensor networks has not been extensively studied in the 5G frequency band. This study is necessary for the proper coverage planning and efficient operation of wireless sensors in various applications such as monitoring summer sporting activities, and environmental/ground surveillance on coastal pebble/sand environments, or tracking pebble mobility and including the rescue of the flood-type avalanche in Gravel-Bed Rivers. In this study, empirical path loss models are proposed for wireless sensor networks in pebble/sand environments at two discrete frequencies, namely 3.5 and 4.2 GHz. The theoretical models and proposed models are compared to indicate the accuracy of proposed models in predicting the path loss in these environments. Additionally, R-squared and RMSE values of eight different generated models are calculated in the range of 0.931–0.877 and 2.284–2.837, respectively. These comparisons indicate that empirical model parameters have a significant effect on the path loss model.

Mobile sensor patrol path planning in partially observable border regions

A border surveillance operation requires sophisticated sensor planning, as sensors are usually scarce and cannot cover an entire region simultaneously. Border patrol agents act as moving sensors in a border region, and the border patrol agents’ coverage moves around the region dynamically, increasing the chance of approaching a trespassing agent. Typically, the locations of trespassing agents cannot be fully observed due to the size of the border region and obstacles. In addition, intelligent trespassing agents may dynamically adjust their traveling paths so that the border patrol agents cannot predict their locations easily. Trespassing agents are assumed to leave traces that indicate their footprints, providing their estimated locations. Border patrol agents may use the trespassers’ footprints as partial information to leverage patrol path planning. We propose an adaptive border patrol process as a partially observable Markov decision process (POMDP), in which an individual border patrol agent’s decision is determined dynamically on the basis of trespassing agents’ partially observed locations. The observations are shared among individual border patrol agents, allowing the border patrol agents to cooperate. The zoning technique is used to limit the planning scope of an individual border patrol agent, and Monte Carlo simulation is applied to reduce the complexity of the POMDP planning problem. Empirical experiments are conducted by means of simulated agents. The simulation parameters are derived from the interviews with a group of border patrol experts. The results in different scenarios show that the proposed patrol path planning scheme outperforms other patrol path planning schemes in terms of the trespasser detection rate. The simulation results are validated with respect to subject matter experts (SMEs), where SMEs are the same border patrol experts who had given the interviews. The proposed method has potential in border surveillance as an assisting system for human border patrol or an automated guidance system in robots or drones.

Assembly specific viewpoint generation as part of a simulation based sensor planning pipeline

Manual assembly often lacks process monitoring since information feedback is only generated by the worker. Machine vision systems thereby offer the possibility to automatically derive process states without intervening the work in progress. Recent approaches in the field of sensor planning and advances in sensor simulation allow automatic machine vision design but simultaneously require detailed knowledge of relevant object features which is difficult to obtain. Therefore, this paper introduces a novel method to derive task-specific viewpoints for the inspection of assembly states supporting a simulation-based pipeline to configure the inspection setup automatically.

A Collaborative Planning Method of Space-Ground Sensor Network Coverage Optimization for Multiparameter Observation Tasks

A multiparameter observation task includes a comprehensive theme with multiple indispensable parameters that need to be monitored simultaneously. However, with limited observation sensor resources for a multiparameter observation task, the spatial misalignment of the coverage area of each parameter decreases the observation efficiency, especially in a space-ground sensor network. To solve this problem, we developed a collaborative planning method in the sensor planning phase. With this method, a space-ground maximal coverage model with multiple parameters (SGMC-MP) was introduced. The proposed collaborative planning method cooperatively utilizes the space-ground sensors to make an observation plan. This method aims to maximize the overlay coverage range among the parameters in the task to reduce the spatial misalignment and improve the utilization of the sensors. The proposed method was applied to a multiparameter observation task in the Three Gorges Reservoir Area in Chongqing. The results indicate that the proposed method exhibits better coverage performance for sensor planning in the multiparameter observation task than the traditional separate planning method. In addition, planning strategies, coverage flexibility, model extension, and algorithm comparison are further discussed.

Mobile sensors' patrol path planning in unobservable border region

PurposeThe border control becomes challenging when a protected region is large and there is a limited number of border patrols. This research paper proposes a novel heuristic-based patrol path planning scheme in order to efficiently patrol with resource scarcity.Design/methodology/approachThe trespasser influencing score, which is determined from the environmental characteristics and trespassing statistic of the region, is used as a heuristic for measuring a chance of approaching a trespasser. The patrol plan is occasionally updated with a new trespassing statistic during a border operation. The performance of the proposed patrol path planning scheme was evaluated and compared with other patrol path planning schemes by the empirical experiment under different scenarios.FindingsThe result from the experiment indicates that the proposed patrol planning outperforms other patrol path planning schemes in terms of the trespasser detection rate, when more environment-aware trespassers are in the region.Research limitations/implicationsThe experiment was conducted through simulated agents in simulated environment, which were assumed to mimic real behavior and environment.Originality/valueThis research paper contributes a heuristic-based patrol path planning scheme that applies the environmental characteristics and dynamic statistic of the region, as well as a border surveillance problem model that would be useful for mobile sensor planning in a border surveillance application.

Dynamic Model Node Scheduling Algorithm Along with OBSP Technique to Schedule the Node in the Sensitive Cluster Region in the WSN

In large-scale applications in science and business space require the exchange of enormous information over high performance systems for remote tasks. In wireless sensor networks, the sensor planning is a appropriate set up for controlling the node energy utilization and expanding network coverage lifetime. The task is to decide a proper planning component for the nodes with the end goal to keep up adequate check of dynamic nodes for maximum network coverage. In this paper we demonstrate the node scheduling algorithm and discuss about the transmission scheduling technique. The node can be described in various level of simulation with described active nodes. The life time of the active node will describes in the simulation diagram. The scheduling technique of the node can be clearly defined in the proposed algorithm to communicate the information of the node within the clusters. Moreover the proposed dynamic model node scheduling algorithm to schedule the node within in the cluster region. And we propose the few techniques of OBSP to know the status of the node and its various levels. In our work we clearly demonstrate the three states of the node. The cluster connection will be done with the help of the gateway node.

High-quality meets low-cost: Approaches for hybrid-mobility sensor networks

Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.

Evaluation of Wireless Body Area Networks

Remote body Area Network is the interconnection of different hubs that are situated in or around the outside of the body which is equipped for remote correspondence. Remote body Area Network includes different observing application condition, fighting, farming, military and social insurance. The principle motivation behind WBAN is to physiologically screen patient's fundamental signs and thus course the related information towards a base station. The sensor hubs are normally light weight, minimal effort, low power expending insight gadgets which are equipped for detecting, figuring, speaking with one another remotely. This audit gives an unmistakable outline about the elements of WBAN. The fundamental activity of conventions, transmitter and beneficiary of IEEE 802.15.6 are profoundly analyzed and examined in this work. The WBAN elements include the sensor devices .These sensor devises are wearable devices. These sensor devices are the detector devices such as motion sensors, ECG sensors etc. There are some protocols which are within body protocols in WBAN. These protocols enhance the work of the Wireless Body area network There are also some security aspects including confidentiality, integrity in WBAN. There are various application areas where these sensors can be used .This review paper gives snappy synopsis about the sensor plan, WBAN architecture, within-body routing protocols, applications, control proficiency, vitality preservation, correspondence conventions and security issues in WBAN.

Robust Optimal Sensor Planning for Occlusion Handling in Dynamic Robotic Environments

Optimal sensor planning for workspace detection in robotic environments is hindered due to sensor occlusions. These occlusions are often dynamic. Probabilistic optimization frameworks, which generally deal with the uncertain nature of these occlusions, suffer from unreliability and/or unavailability of probability distribution functions. This paper proposes and analyzes a robust optimization approach (minimax), which generates sensor configurations based on occlusion scenarios that cause maximum obstruction of the robotic workspace. The optimal solution is independent of probability distribution functions and provides a guaranteed level of workspace visibility regardless of occluder positions, thus accounting for random occlusions. The method also allows the user to determine the impact of the worst case occlusion scenarios leading to a broader perspective on sensor planning. Evaluation of the approach for a mobile medical X-ray robotic system in a simulation healthcare environment shows the effectiveness of the proposed method.

Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse.

The production of tomatoes in greenhouses, in addition to its relevance in nutrition and health, is an activity of the agroindustry with high economic importance in Spain, the first exporter in Europe of this vegetable. The technological updating with precision agriculture, implemented in order to ensure adequate production, leads to a deployment planning of wireless sensors with limited coverage by the attenuation of radio waves in the presence of vegetation. The well-known propagation models FSPL (Free-Space Path Loss), two-ray, COST235, Weissberger, ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), offer values with an error percentage higher than 30% in the 2.4 GHz band in relation to those measured in field tests. As a substantial improvement, we have developed optimized propagation models, with an error estimate of less than 9% in the worst-case scenario for the later benefit of farmers, consumers and the economic chain in the production of tomatoes.

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.

Mutual Information Maximization-Based Collaborative Data Collection With Calibration Constraint

People pay greater attention to air quality which is closely related to their health, especially in developing countries. Air quality is a part of the Chinese weather forecast, and the government has developed air quality monitoring systems and built high-quality monitoring stations (HQMS). With the data from HQMS, many companies and research institutes demonstrate an accurate air pollution map on the Internet, which is valuable for many issues related to air quality, including exposure modeling and urban planning. Due to the high equipment and operating costs, the distribution of HQMS is too uneven and sparse to achieve a high-resolution air pollution map. Thus, people try to deploy a large number of high precision sensors in and around the city for detecting air pollution. However, these sensors require frequent calibrations with the HQMS to maintain data accuracy. On the other side, to reduce the cost of sensor deployment, people begin to use mobile sensors instead of fixed sensors, which make sensor route planning a very important issue. Nevertheless, existing works on the route planning of mobile sensors largely focus on data reconstruction, which either ignores calibration or views it as an independent problem. In this paper, we propose a novel scheme to improve the accuracy of data reconstruction, which jointly considers sensor calibration and data reconstruction in route planning for mobile sensors. We formulate a novel sensor route planning problem (SRPP) that aims to maximize the mutual information and guarantee the accuracy of measurements through the sensor calibration. We also propose a heuristic algorithm to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. The extensive simulation results well justify the effectiveness of our approach that can reduce 83% root mean square error on average compared with the traditional approach.

On periodic optimal solutions of persistent sensor planning for continuous-time linear systems

This work investigates informative planning of sensing agents over infinite time horizon when the system of interest is expressed as a continuous-time linear system. The objective of this planning problem, termed persistent monitoring problem, is to maintain the monitoring uncertainty at the minimum. The method reduces the persistent planning problem into a periodic planning problem; it is formulated as a periodic optimal control or optimization problem to determine the optimal periodic sensor plan as well as the period. The plan induces the periodic Riccati equation and is proven to lead an arbitrary initial uncertainty state to the optimized periodic trajectory. It is also proven that any infinite-horizon (non-periodic) sensor plan is able to be approximated arbitrary well by a periodic sensor plan. A suboptimal filtering mechanism is proposed by using the resulting optimal periodic solution. Two numerical examples on (a) a relaxed periodic sensor scheduling for a two dimensional linear system, and (b) persistent monitoring by a mobile sensor of two-dimensional diffusion dynamics show the validity of the proposed approach.