Role-based Bayesian decision framework for autonomous unmanned systems

In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.

Assessment of Meteorological Threats to the Coordinated Search and Rescue of Unmanned/Manned Aircraft

The architecture and working principle of coordinated search and rescue system of unmanned/manned aircraft,which is composed of manned/unmanned aircraft and manned aircraft,were first introduced,and they can cooperate with each other to complete a search and rescue task.Secondly,a threat assessment method based on meteorological data was proposed,and potential meteorological threats,such as storms and rainfall,can be predicted by collecting and analyzing meteorological data.Finally,an experiment was carried out to evaluate the performance of the proposed method in different scenarios.The experimental results show that the coordinated search and rescue system of unmanned/manned aircraft can be used to effectively assess meteorological threats and provide accurate search and rescue guidance.

Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts

Peanut (Arachis hypogaea L.) is a highly nutritious food that is an excellent source of protein and is associated with increased coronary health, lower risk of type-2 diabetes, lower risk of breast cancer and a healthy profile of inflammatory biomarkers. The domestic demand for organic peanuts has significantly increased, requiring new breeding efforts to develop peanut varieties adapted to the organic farming system. The use of unmanned aerial system (UAS) has gained scientific attention because of the ability to generate high-throughput phenotypic data. However, it has not been fully investigated for phenotyping agronomic traits of organic peanuts. Peanuts are beneficial for cardio system protection and are widely used. Within the U.S., peanuts are grown in 11 states on roughly 600,000 hectares and averaging 4500 kg/ha. This study’s objective was to test the accuracy of UAS data in the phenotyping pod and seed yield of organic peanuts. UAS data was collected from a field plot with 20 Spanish peanut breeding lines on July 07, 2021 and September 27, 2021. The study was a randomized complete block design (RCBD) with 3 blocks. Twenty-five vegetation indices (VIs) were calculated. The analysis of variance showed significant genotypic effects on all 25 vegetation indices for both flights (p < 0.05). The vegetation index Red edge (RE) from the first flight was the most significantly correlated with both pod (r = 0.44) and seed yield (r = 0.64). These results can be used to further advance organic peanut breeding efforts with high-throughput data collection.

Resilience approach for heterogeneous distributed networked unmanned weapon systems

Disconnection in the distributed heterogeneous networked unmanned weapon systems is caused by multiple weapon units＇ failure. The technical routes were analyzed to achieve resilience in the disconnection situation. A heterogeneous distributed network model of networked unmanned weapon systems was established. And an approach of adding relay weapon units was proposed to a- chieve fault tolerance after weapon units＇ failure due to attack or energy exhaustion. An improved ge- netic algorithm was proposed to determine and optimize the position of the relay weapon units. Simulation results in the MATLAB show that the improved resilience-based genetic algorithm can restore the network connection maximally when the number of relay units is limited, the network can keep on working after failure, and the implementation cost is controlled in a reasonable range.

Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

This paper is the first in a two-part series that introduces an easy-to-implement central command architecture for high-order autonomous unmanned aerial systems. This paper discusses the development and the second paper presents the flight test results. As shown in this paper, the central command architecture consists of a central command block, an autonomous planning block, and an autonomous flight controls block. The central command block includes a staging process that converts an objective into tasks independent of the vehicle (agent). The autonomous planning block contains a non-iterative sequence of algorithms that govern routing, vehicle assignment, and deconfliction. The autonomous flight controls block employs modern controls principles, dividing the control input into a guidance part and a regulation part. A novel feature of high-order central command, as this paper shows, is the elimination of operator-directed vehicle tasking and the manner in which deconfliction is treated. A detailed example illustrates different features of the architecture.

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

Analysis of unmanned aerial vehicle navigation and height control system based on GPS

According to the characteristic of global positioning system（GPS） reflection signals,a GPS delay mapping receiver system scheme is put forward,which not only satisfies the unmanned aerial vehicle（UAV） guidance localization but also realizes height measurement.A code delay algorithm is put forward,which processes the direct and land reflected signal and outputs the navigation data and specular point.The GPS terrain reflected echo signal mathematical equation is inferred.The reflecting signal area,when the GPS signal passes the land,is analyzed.The height survey model reflected land surface characteristic is established.A simulation system which carries guidance localization of the UAV and the height measuring control through the GPS direct signal and the land reflected signal is designed,taken the GPS satellite as the illumination source,the receiver is put on the UAV.Then the UAV guidance signal,the GPS reflection signal and receiver＇s parallel processing are realized.The parallel processing reduces UAV＇s payload and raises system＇s operating efficiency.The simulation results confirms the validity of the model and also provides the basis for the UAV＇s optimization design.

Asynchronous H_∞ Control for Unmanned Aerial Vehicles: Switched Polytopic System Approach

This study is concerned with the H∞control for the full-envelope unmanned aerial vehicles(UAVs) in the presence of missing measurements and external disturbances. With the dramatic parameter variations in large flight envelope and the locally overlapped switching laws in flight, the system dynamics is modeled as a locally overlapped switched polytopic system to reduce designing conservatism and solving complexity. Then,considering updating lags of controller s switching signals and the weighted coefficients of the polytopic subsystems induced by missing measurements, an asynchronous H∞control method is proposed such that the system is stable and a desired disturbance attenuation level is satisfied. Furthermore, the sufficient existing conditions of the desired switched parameter-dependent H∞controller are derived in the form of linear matrix inequality(LMIs) by combining the switched parameter-dependent Lyapunov function method and average dwell time method.Finally, a numerical example based on a highly maneuverable technology(Hi MAT) vehicle is given to verify the validity of the proposed method.

Design of Adaptive Fuzzy PID Altitude Control System for Unmanned Aerial Vehicle

Based on Matlab/Simulink and Fuzzy Logic toolboxes, the altitude control system is designed and simulated. The validity of conventional PID control method and adaptive fuzzy PID control method is compared. It can be drawn out that the adaptive fuzzy PID control method is superior to the conventional PID in rising time and overshoot etc. The effectiveness of a fuzzy PID controller shows potential application in the future, especially in the presence of model uncertainty or changing dynamics and time-varying parameters.

Initiation of Unmanned Aerial System (UAS) Phenotyping of Plant Height and Canopy Width in Guar

The use of the Unmanned Aerial System (UAS) has attracted scientific attention because of its potential to generate high-throughput phenotyping data. The application of UAS to guar phenotyping remains limited. Guar is multi-purpose legume species. India and Pakistan are the world’s top guar producers. The U.S. is the world guar largest market with an import value of >$1 billion annually. The objective of this study was to test the feasibility of UAS phenotyping of plant height and canopy width in guar. The UAS data were collected from a field plot of 10 guar accessions on July 7, 2021, and September 27, 2021. The study was organized in a Randomized Complete Block Design (RCBD) with 3 blocks. A total of 23 Vegetation Indices (VIs) were computed. The analysis of variance showed significant genotypic effects on plant weight (p < 0.05) and canopy width (p on plant height (p most VIs were significant for both flights (p Vegetation Index (NDVI) and Red Edge Normalized Difference Vegetation Index (NDRE) were significantly and highly correlated with plant height (r = 0.74) and canopy width (r = 0.68). The results will be of interest in developing high throughput phenotyping approach for guar breeding.

Distributed tracking control of unmanned aerial vehicles under wind disturbance and model uncertainty

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controller is designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method. ? 2016 Beijing Institute of Aerospace Information.

Technology Development of Unmanned Underwater Vehicles (UUVs)

In recent years, the weapon systems have been changing drastically because of the advancement of science technology and the change of military concept of combat. There is an unmanned system at the center of all those changes. Especially, in case of maritime environment, as the center stage of combat has changed from ocean to coastal areas, it is difficult for the existing naval forces to effectively operate in shallow waters. Therefore, unmanned underwater vehicles (UUVs) are being required at an increasing pace. In this paper, we analyze the characteristics of already developed UUVs, which are the key unmanned system of the marine battlefield environment in the future. Through the analysis of development cases and the investigation of the essential technologies, the critical design issues of UUVs are elaborated. We also suggest the future directions of the UUV technologies based on the case analysis.

Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

In recent years, because of the development of marine military science technology, there is a growing interest in the unmanned systems throughout the world. Also, the demand of Unmanned Surface Vehicles (USVs) which can be autonomously operated without the operator intervention is increasing dramatically. The growing interests lie in the facts that those USVs can be manufactured at much lower costs, and can be operated without the human fatigue, while can be sent to the hostile or quite dangerous areas that are inherently unhealthy for human operators. The utilization and the deployment of such vessels will continue to grow in the future. In this paper, along with the technological development of unmanned surface vehicles, we investigate and analyze the cases of already developed platforms and identify the trends of the technological advances. Additionally, we suggest the future directions of development.

Automatic Carrier Landing Control for Unmanned Aerial Vehicles Based on Preview Control

For carrier-based unmanned aerial vehicles(UAVs),one of the important problems is the design of an automatic carrier landing system(ACLS)that would enable the UAVs to accomplish autolanding on the aircraft carrier.However,due to the movements of the flight deck with six degree-of-freedom,the autolanding becomes sophisticated.To solve this problem,an accurate and effective ACLS is developed,which is composed of an optimal preview control based flight control system and a Kalman filter based deck motion predictor.The preview control fuses the future information of the reference glide slope to improve landing precision.The reference glide slope is normally a straight line.However,the deck motion will change the position of the ideal landing point,and tracking the ideal straight glide slope may cause landing failure.Therefore,the predictive deck motion information from the deck motion predictor is used to correct the reference glide slope,which decreases the dispersion around the desired landing point.Finally,simulations are carried out to verify the performance of the designed ACLS based on a nonlinear UAV model.

UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society

Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges,with their advantages including high performance,efficiency,flexibility,and inherent resilience.Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence(AI).However,the current networked unmanned systems are primarily designed for and applicable to a narrow range of domain-specific missions,and do not have sufficient human-level intel-ligence and human needs fulfillment for the challenging missions in our lives.We propose in this paper a vision of human-centric networked unmanned systems:Unmanned Intelligent Cluster(UnIC).Within this vision,distributed unmanned systems and humans are connected via knowledge sharing and social awareness to achieve collaborative cognition.This paper details UnIC’s concept,sources of intelligence,and layered architecture,and reviews enabling technologies for achieving this vision.In addition to the technological aspects,the social acceptance issues are highlighted.

A new robust fuzzy method for unmanned flying vehicle control

A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.