Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.

Hierarchical planning for a surface mounting machine placement

For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach was implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

UAV trajectory planning algorithmfor data collection in wireless sensor networks

In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.

Sensor Robot Planning in Incomplete Environment

Aiming at the former formalized methods of robot planning should give the environment state, can not obtain the new knowledge of the environment. In order to improve the reason ability for obtaining new knowledge of the environment state, the actions in the process of planning such as external action and sensing action are formalized. A formalized reasoning method—CPNI (Colored Petri Net for Planning in incomplete environment) based on two kinds of actions is proposed, and the reasoning rule as Fluent Calculus in incomplete environment is applied. Robot planning experiment is modeled and simulated by using the tool CPNTools and the result shows the state knowledge of the door and the action sequence to reach the goal can be generated automatically in the CPNI net system.

The Safe Navigation of Partial Motion Planning Based on “Cooperation” with Roadside Fixed Sensors in VANET

In recent years, many methods of safe vehicle navigation and partial motion planning (PMP) have been proposed in vehicular ad-hoc network (VANET) field. In order to improve the limitation of traditional PMP, this paper presents a novel effective way to plan motion with cooperation of roadside fixed sensors (RFSs). With their cooperation, the vehicles can get the surrounding information quickly and effectively, and give highly accurate projections about the near future conditions on road. After proposing our algorithm, the worst case is analyzed and methods are found to solve the problem. Finally we conduct one elemental contrast experiment, driver situation awareness, with or without the “cooperation” of RFSs in highway scenarios. The result shows that the vehicles can make a better PMP based on the forward conditions received from RFSs, and extend the warning distance obviously when emergency happens.

Optimal sensor placement in hydropower house based on improved triaxial effective independence method

The capacity and size of hydro-generator units are increasing with the rapid development of hydroelectric enterprises, and the vibration of the powerhouse structure has increasingly become a major problem. Field testing is an important method for research on dynamic identification and vibration mechanisms. Research on optimal sensor placement has become a very important topic due to the need to obtain effective testing information from limited test resources. To overcome inadequacies of the present methods, this paper puts forward the triaxial effective independence driving-point residue （EfI3-DPR3） method for optimal sensor placement. The Efl3-DPR3 method can incorporate both the maximum triaxial modal kinetic energy and linear independence of the triaxial target modes at the selected nodes. It was applied to the optimal placement oftriaxial sensors for vibration testing in a hydropower house, and satisfactory results were obtained. This method can provide some guidance for optimal placement of triaxial sensors of underground powerhouses.

Numerical Simulation of Methane Distribution and Sensor Placement in 2-Dimension Roadway

In order to provide a theoretical basis for methane sensor placement in the vertical direction of a tunnel,the software Fluent was used to simulate methane distribution. A geometric roadway model was established and divided by grids. Methane distribution in both level and vertical sections was simulated using a realizable k-ε model with the Fluent software according to a conservation equation in a turbulent state,a turbulent kinetic energy equation and a turbulent dissipation rate equation. The realizable k-ε model and the Fluent software were used to simulate methane distribution according to the principle of the conservation equation in a state of turbulent flow. The results show that after overflow-ing,a methane level with a certain thickness is formed. Methane density curves at three specific levels were internally consistent and methane density at higher levels is denser than that at lower levels. Methane distribution becomes thinner in the direction of wind and methane in the vertical direction becomes uniform if wind speed is high. The distance be-tween sensors and roof should be less than 300 mm which is in agreement with mine safety regulations.

Multi-type sensor placement and response reconstruction for building structures: Experimental investigations

Estimation of lateral displacement and acceleration responses is essential to assess safety and serviceability of high-rise buildings under dynamic loadings including earthquake excitations. However, the measurement information from the limited number of sensors installed in a building structure is often insufficient for the complete structural performance assessment. An integrated multi-type sensor placement and response reconstruction method has thus been proposed by the authors to tackle this problem. To validate the feasibility and effectiveness of the proposed method, an experimental investigation using a cantilever beam with multi-type sensors is performed and reported in this paper. The experimental setup is first introduced. The finite element modelling and model updating of the cantilever beam are then performed. The optimal sensor placement for the best response reconstruction is determined by the proposed method based on the updated FE model of the beam. After the sensors are installed on the physical cantilever beam, a number of experiments are carried out. The responses at key locations are reconstructed and compared with the measured ones. The reconstructed responses achieve a good match with the measured ones, manifesting the feasibility and effectiveness of the proposed method. Besides, the proposed method is also examined for the cases of different excitations and unknown excitation, and the results prove the proposed method to be robust and effective. The superiority of the optimized sensor placement scheme is finally demonstrated through comparison with two other different sensor placement schemes： the accelerometer-only scheme and non-optimal sensor placement scheme. The proposed method can be applied to high-rise buildings for seismic performance assessment.

SVD Approach for Actuator and Sensor Placement in Active Vibration Control of Large Cable Net Structures

The actuator and sensor placement problem for active vibration control of large cable net structures is investigated in this paper.Since the structures exhibit closely spaced modes in the range of low frequencies,the number of modes to be considered is quite large after modal truncation,while only a limited number of actuators and sensors are to be placed.This makes it hard to determine the actuator and sensor locations with the existing placement methods in the literature such as the methods based on the controllability/observability grammian.To deal with this issue,an actuator and sensor placement method based on singular value decompositions(SVD)of the input and output matrices is proposed,which guarantees the modal controllability and observability of the system.The effectiveness of the SVD based method is verified through numerical simulations in which comparisons are conducted between randomly-chosen locations and the optimal ones obtained by a genetic algorithm.

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area.One of the simplest and most efficient algorithms,the artificial potential field algorithm(APF),may provide real-time navigation in those places but fall into local mini-mum in some cases.To overcome this problem and to present alternative escape routes for a robot,possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm.This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows,doors,and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm.The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging(LIDAR)sensors to navigate through an unknown closed space and reach a desired goal point.Inclusion of crossing points allows the robot to escape from areas where it is con-gested.The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods.The results showed that the improved APF methods and the methods rein-forced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room.For the goals outside the current room,traditional APF methods were quite unsuccessful in reaching the goals.Even though improved methods were able to reach some outside targets,the proposed method gave approximately 17%better results than the most success-ful example in achieving targets outside the current room.The proposed method can also work in real-time to discover a building and navigate between rooms.

3DT-PP:localization and path planning of mobile anchors over complex 3D terrains

Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.

Automatic Construction of Floor Plan with Smartphone Sensors

Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.

Sensor placement for structural damage detection with modal data

A new method is presented for prioritizing sensor locations for structural health monitoring (SHM). In view of the needs of SHM and damage detection,sensor locations are optimized for the purpose of both sensitivity for local damages and independence of the target mode. However,the two different optimization criterions lead to an inconsistency of the optimal result. Considering the structural response changes that result from damage,the relationship between the structural response and damage is deduced from the structural motion equation by a quasi-analytical mode. Based on the harmony between damage identifiability and mode observability,an object function is set up,including the information of mode independence and damage sensitivity. Utilizing the technique of singular value decomposition,an interior algorithm for the optimum sensor placement is proposed with the multiple objective criterions of minimizing the condition number of coefficient matrix and maximizing the fisher information matrix. A numerical example shows that this approach can effectively avoid the contradiction between the two different optimization criterions. Comparing with the result of single object,the result of damage detection from the optical sensor locations is much more accurate.

Sensor placement of long-term health monitoring for large bridges based on the real-time correction of finite element model

The process of optimized placement of long-term health monitoring sensors for large bridges generally begins with finite element models, but there will arise great discrepancies between theoretically-calculated results and actual measurements.Therefore, rectified finite element models need to be rectified by virtue of model rectifying technology. Firstly, the result of construction monitoring and finished state load test is used to real-time modification of finite element model. Subsequently, an accurate finite element model is established. Secondly, the optimizing the layout of sensor with following orthogonality guarantees orthogonal property and linear independence for the measured data. Lastly, the effectiveness and feasibility of method in the paper is tested by real-time modifying finite element model and optimizing the layout of sensor for Nujiang Bridge.

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner.We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption.Measured data is modeled as a Gaussian random variable with a changeable variance.A gird model is used to approximate the problem.We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search.Our experiments demonstrate that the algorithm is correct in a certain tolerance,and it is also efficient and scalable.

Sensor Placement to Improve the Positioning Performance Based on Angle of Arrival (AOA)

In this paper, a method for sensor placement to improve the placement quality based on angle of arrival of signal in a specific area is proposed. The installation place of sensors may be constrained with specified boundaries. In this me-thod, the criterion of maximum quality of placement is the Cramer-Rao bound. The generalized pattern search as an effective method is used to maximize error bound of the placement problem by angle of arrival. Better results are ob-tained in comparison with results of genetic algorithm. The derived results are compared from two aspects of run time and result quality.

Optimal sensor placement for structural response estimation

A methodology, termed estimation error minimization(EEM) method, was proposed to determine the optimal number and locations of sensors so as to better estimate the vibration response of the entire structure. Utilizing the limited sensor measurements, the entire structure response can be estimated based on the system equivalent reduction-expansion process(SEREP) method. In order to compare the capability of capturing the structural vibration response with other optimal sensor placement(OSP) methods, the effective independence(EI) method, modal kinetic energy(MKE) method and modal assurance criterion(MAC) method, were also investigated. A statistical criterion, root mean square error(RMSE), was employed to assess the magnitude of the estimation error between the real response and the estimated response. For investigating the effectiveness and accuracy of the above OSP methods, a 31-bar truss structure is introduced as a simulation example. The analysis results show that both the maximum and mean of the RMSE value obtained from the EEM method are smaller than those from other OSP methods, which indicates that the optimal sensor configuration obtained from the EEM method can provide a more accurate estimation of the entire structure response compared with the EI, MKE and MAC methods.

An Approach for Damage Identification and Optimal Sensor Placement in Structural Health Monitoring by Genetic Algorithm Technique

Civil engineering structures are constructed for strength, serviceability and durability. The structures thus constructed involve huge investment and labour work. In order to protect the structure from various damages, periodic monitoring of structures is necessary. Hence Structural Health Monitoring (SHM) plays a vital role in diagnosing the state of the structure at every moment during its life period. For this purpose, sensors are deployed in the structures for its efficient health monitoring. Sensors cannot be deployed at random locations of the structure. They have to be located at those points which reflect the damage. In this study, a 3-storey and a 4-storey building are taken and Modal Strain Energy (MSE) is used for finding the initial locations of sensors. The number of sensors obtained is then optimized using Genetic Algorithm (GA) technique. Finally damages are induced in certain locations of the structure and a damage detection technique called as “Flexibility Matrix Based Technique (FMBT)” is introduced for damage localization in the structure.

Multi-objective Optimal Trajectory and Placement of a Robot for Cotton Yarns Handling

This study deals with a robot manipulator for yarn bobbin handling in the cotton yarns lattice distortion modification system.The aim is to achieve an operation of yarn bobbin handling with minimal execution time,energy consumption and jerk in motion together.The placement of the robot,in relation to the yarn bobbin stations,is also optimized in conjunction of trajectory optimization.Three possible techniques for building the handling traj'ectory were considered:the quaternion spherical linear interpolation in Cartesian space,the quintic polynomial spline and quintic B-spline in joint space.The genetic algorithm(GA) was used to optimize the trajectories of the robot,with a penalty function to handle nonlinear constraints associated in the robot motion.Two simulations of the optimal trajectory in joint space and the placement of robot were carried out and the results obtained were presented and discussed.It is concluded that the quintic polynomial spline constructs a better trajectory in joint space and the proper placement of robot makes better performance.