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Dynamic value iteration networks for the planning of rapidly changing UAV swarms Research Articles

Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang,li2ui2@zju.edu.cn,boweiy@zju.edu.cn,ghsong@zju.edu.cn,jiangxh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.1900712

Abstract: evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II

Keywords： 动态值迭代网络；场景式Q学习；无人机自组网；NSGA-II；路径规划    

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Motion planning of a quadrotor robot game using a simulation-based projected policy iteration method Regular Papers

Li-dong ZHANG, Ban WANG, Zhi-xiang LIU, You-min ZHANG, Jian-liang AI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 525-537 doi: 10.1631/FITEE.1800571

Abstract:

Making rational decisions for sequential decision problems in complex environments has been challenging researchers in various fields for decades. Such problems consist of state transition dynamics, stochastic uncertainties, long-term utilities, and other factors that assemble high barriers including the curse of dimensionality. Recently, the state-of-the-art algorithms in reinforcement learning studies have been developed, providing a strong potential to efficiently break the barriers and make it possible to deal with complex and practical decision problems with decent performance. We propose a formulation of a velocity varying one-on-one quadrotor robot game problem in the threedimensional space and an approximate dynamic programming approach using a projected policy iteration method for learning the utilities of game states and improving motion policies. In addition, a simulation-based iterative scheme is employed to overcome the curse of dimensionality. Simulation results demonstrate that the proposed decision strategy can generate effective and efficient motion policies that can contend with the opponent quadrotor and gather advantaged status during the game. Flight experiments, which are conducted in the Networked Autonomous Vehicles (NAV) Lab at the Concordia University, have further validated the performance of the proposed decision strategy in the real-time environment.

Keywords： Reinforcement learning     Approximate dynamic programming     Decision making     Motion planning     Unmanned aerial vehicle    

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A Cyber-Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks Article

Die Hu, Shaoshi Yang, Min Gong, Zhiyong Feng, Xuejun Zhu

Engineering 2022, Volume 19, Issue 12,   Pages 217-227 doi: 10.1016/j.eng.2021.10.022

Abstract:

As a special type of mobile ad hoc network (MANET), the flying ad hoc network (FANET) has the potential to enable a variety of emerging applications in both civilian wireless communications (e.g., 5G and 6G) and the defense industry. The routing protocol plays a pivotal role in FANET. However, when designing the routing protocol for FANET, it is conventionally assumed that the aerial nodes move randomly. This is clearly inappropriate for a mission-oriented FANET (MO-FANET), in which the aerial nodes typically move toward a given destination from given departure point(s), possibly along a roughly deterministic flight path while maintaining a well-established formation, in order to carry out certain missions. In this paper, a novel cyber-physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration, which makes full use of the mission-determined trajectory dynamics to construct the time sequence of rejoining and separating, as well as the adjacency matrix for each node, as prior information. Compared with the existing representative routing protocols used in FANETs, our protocol achieves a higher packet-delivery ratio (PDR) at the cost of even lower overhead and lower average end-to-end latency, while maintaining a reasonably moderate and stable network jitter, as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.

Keywords： Cyber-physical system     Flying ad hoc network (FANET)     Routing protocol     Trajectory dynamics     Unmanned aerial vehicle (UAV)    

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UAV search-and-rescue planning using an adaptive memetic algorithm Research Articles

Libin Hong, Yue Wang, Yichen Du, Xin Chen, Yujun Zheng,yujun.zheng@computer.org

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 11,   Pages 1477-1491 doi: 10.1631/FITEE.2000632

Abstract: The use of unmanned aerial vehicles (UAVs) is becoming more commonplace in tasks, but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive s, and adaptive s on a wide set of problem instances.

Keywords： 文化基因算法；自适应；无人机；搜救    

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Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

Hongbo REN, Yinlong LU, Qiong WU, Xiu YANG, Aolin ZHOU

Frontiers in Energy 2018, Volume 12, Issue 4,   Pages 518-528 doi: 10.1007/s11708-018-0594-7

Abstract: To solve the optimization model, the non-dominated sorting generic algorithm II (NSGA-II) is employedThe results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto

Keywords： multi-objective optimization     hybrid distributed energy system     non-dominated sorting generic algorithm II    

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Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization Research

Wan-ying Ruan, Hai-bin Duan,wyruan@buaa.edu.cn,hbduan@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5,   Pages 649-808 doi: 10.1631/FITEE.2000066

Abstract: We propose multi-objective social learning (MSLPIO) and apply it to for formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We simulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective and the improved non-dominated sorting genetic algorithm.

Keywords： 无人机；避障；鸽群优化；多目标社会学习鸽群优化    

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The Development and Research Synopsis of Large UAV in China

Zhao Xu

Strategic Study of CAE 2003, Volume 5, Issue 1,   Pages 38-41

Abstract:

This paper systematically reviews the history and background of large UAV * s development in China, which includes the first developed simulated target drone, all kinds of substantial pilotless drone and the developing unmanned attack air vehicle. The main technical difficulties and key technologies applied are introduced in detail. The developing journey of all kinds of unmanned air vehicle is formulised. In the end, the importance and development of UCAV in China are also previewed, analysed and plotted.

Keywords： target drone     unmanned air vehicle (UAV)     unmanned combat air vehicle (UCAV)     development     application    

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Secure connectivity analysis in unmanned aerial vehicle networks None

Xin YUAN, Zhi-yong FENG, Wen-jun XU, Zhi-qing WEI, Ren-ping LIU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 409-422 doi: 10.1631/FITEE.1700032

Abstract: The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.

Keywords： Unmanned aerial vehicle networks (UAVNs)     Trust model     Secure connectivity     Doppler shift    

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Anewhierarchical software architecture towards safety-critical aspects of a drone system Special Feature on Intelligent Robats

Xiao-rui ZHU, Chen LIANG, Zhen-guo YIN, Zhong SHAO, Meng-qi LIU, Hao CHEN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 353-362 doi: 10.1631/FITEE.1800636

Abstract:

A new hierarchical software architecture is proposed to improve the safety and reliability of a safetycritical drone system from the perspective of its source code. The proposed architecture uses formal verification methods to ensure that the implementation of each module satisfies its expected design specification, so that it prevents a drone from crashing due to unexpected software failures. This study builds on top of a formally verified operating system kernel, certified kit operating system (CertiKOS). Since device drivers are considered the most important parts affecting the safety of the drone system, we focus mainly on verifying bus drivers such as the serial peripheral interface and the inter-integrated circuit drivers in a drone system using a rigorous formal verification method. Experiments have been carried out to demonstrate the improvement in reliability in case of device anomalies.

Keywords： Safety-critical     Drone     Software architecture     Formal verification    

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Minimax Q-learning design for H∞ control of linear discrete-time systems Research Articles

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 438-451 doi: 10.1631/FITEE.2000446

Abstract: The method is an effective approach for attenuating the effect of disturbances on practical systems, but it is difficult to obtain the ler due to the nonlinear Hamilton–Jacobi–Isaacs equation, even for linear systems. This study deals with the design of an ler for linear discrete-time systems. To solve the related game algebraic Riccati equation (GARE), a novel model-free method is developed, on the basis of an offline algorithm, which is shown to be Newton’s method for solving the GARE. The proposed method, which employs off-policy , learns the optimal control policies for the controller and the disturbance online, using only the state samples generated by the implemented behavior policies. Different from existing -learning methods, a novel gradient-based policy improvement scheme is proposed. We prove that the method converges to the saddle solution under initially admissible control policies and an appropriate positive learning rate, provided that certain persistence of excitation (PE) conditions are satisfied. In addition, the PE conditions can be easily met by choosing appropriate behavior policies containing certain excitation noises, without causing any excitation noise bias. In the simulation study, we apply the proposed method to design an load-frequency controller for an electrical power system generator that suffers from load disturbance, and the simulation results indicate that the obtained load-frequency controller has good disturbance rejection performance.

Keywords： H∞ control     Zero-sum dynamic game     Reinforcement learning     Adaptive dynamic programming     Minimax Q-learning    

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Decentralized fault-tolerant cooperative control of multipleUAVs with prescribed attitude synchronization tracking performance under directed communication topology Regular Papers

Zi-quan YU, Zhi-xiang LIU, You-min ZHANG, Yao-hong QU, Chun-yi SU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 685-700 doi: 10.1631/FITEE.1800569

Abstract:

In this paper, a decentralized fault-tolerant cooperative control scheme is developed for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and a directed communication network. To counteract in-flight actuator faults and enhance formation flight safety, neural networks (NNs) are used to approximate unknown nonlinear terms due to the inherent nonlinearities in UAV models and the actuator loss of control effectiveness faults. To further compensate for NN approximation errors and actuator bias faults, the disturbance observer (DO) technique is incorporated into the control scheme to increase the composite approximation capability. Moreover, the prediction errors, which represent the approximation qualities of the states induced by NNs and DOs to the measured states, are integrated into the developed fault-tolerant cooperative control scheme. Furthermore, prescribed performance functions are imposed on the attitude synchronization tracking errors, to guarantee the prescribed synchronization tracking performance. One of the key features of the proposed strategy is that unknown terms due to the inherent nonlinearities in UAVs and actuator faults are compensated for by the composite approximators constructed by NNs, DOs, and prediction errors. Another key feature is that the attitude synchronization tracking errors are strictly constrained within the prescribed bounds. Finally, simulation results are provided and have demonstrated the effectiveness of the proposed control scheme.

Keywords： Fault-tolerant control     Decentralized control     Prescribed performance     Unmanned aerial vehicle     Neural network     Disturbance observer     Directed topology    

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A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc Research Articles

Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang,ch19930611@zju.edu.cn,lvniqi@gmail.com,ghsong@zju.edu.cn,boweiy@zju.edu.cn,jiangxh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1308-1320 doi: 10.1631/FITEE.1900401

Abstract: In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware enhanced protocol based on greedy perimeter stateless routing (GPSR), for UAV ad-hoc networks. The protocol enforces a strategy using the congestion information of neighbors, and evaluates the quality of a wireless link by the algorithm, which is a algorithm. Based on the evaluation of each wireless link, the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss. We simulate the performance of TQNGPSR and compare it with AODV, OLSR, GPSR, and QNGPSR. Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR. In high node density scenarios, it also outperforms AODV and OLSR in terms of the packet delivery ratio, end-to-end delay, and throughput.

Keywords： Traffic balancing     Reinforcement learning     Geographic routing     Q-network    

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A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities Research Articles

Zhi Zheng, Shuncheng Cai,zhengz@fjnu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 10,   Pages 1334-1350 doi: 10.1631/FITEE.2000362

Abstract: Target tracking is one of the hottest topics in the field of drone research. In this paper, we study the multiple unmanned aerial vehicles () problem. We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile, uncertain, and intelligent motion. For three classic target motion modes, we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints, and propose an intention estimation mechanism based on the environment and target trajectory features. We propose a novel Voronoi diagram, called MDA-Voronoi, which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV. In each MDA-Voronoi region, the maximum reachable region of each UAV is defined, the upper and lower bounds of the trajectory coverage probability are analyzed, and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time. Then, we use the Nash -learning method to design the UAVs’ collaborative tracking strategy, considering factors such as collision avoidance, maneuvering constraints, tracking cost, sensing performance, and path overlap. By designing the reward mechanism, the optimal action strategies are obtained as the control input of the UAVs. Finally, simulation analyses are provided to validate our method, and the results demonstrate that the algorithm can improve the performance for multiple UAVs with inferior tracking capabilities.

Keywords： 协同跟踪；意图估计；MDA-Voronoi图；多无人机；性能不占优    

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Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits Research Article

Ayoub SADEGHI, Razieh GHASEMI, Hossein GHASEMIAN, Nabiollah SHIRI,H.ghasemian@sutech.ac.ir

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 4,   Pages 599-616 doi: 10.1631/FITEE.2200077

Abstract: Carbon nanotube field-effect transistors (CNTFETs) are reliable alternatives for conventional transistors, especially for use in (AC) based error-resilient digital circuits. In this paper, CNTFET technology and the technique are merged, and three new AC-based full adders (FAs) are presented with 6, 6, and 8 transistors, separately. The is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits’ improvement by about 50% in terms of power-delay-product (PDP) at the cost of area occupation. The Monte Carlo method (MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold (DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit (LSB) parts of more complex arithmetic circuits such as multipliers.

Keywords： field-effect transistor (CNTFET)     Optimization algorithm     Nondominated sorting based genetic algorithm II(NSGA-II)     Gate diffusion input (GDI)     Approximate computing    

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Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques Article

Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee

Engineering 2020, Volume 6, Issue 8,   Pages 919-926 doi: 10.1016/j.eng.2020.07.001

Abstract:

Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.

Keywords： Pine wilt disease     Drone remote sensing     Artificial neural network     Support vector machine     Global positioning system    

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Title Author Date Type Operation

Dynamic value iteration networks for the planning of rapidly changing UAV swarms

Wei Li, Bowei Yang, Guanghua Song, Xiaohong Jiang,li2ui2@zju.edu.cn,boweiy@zju.edu.cn,ghsong@zju.edu.cn,jiangxh@zju.edu.cn

Journal Article

Motion planning of a quadrotor robot game using a simulation-based projected policy iteration method

Li-dong ZHANG, Ban WANG, Zhi-xiang LIU, You-min ZHANG, Jian-liang AI

Journal Article

A Cyber-Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks

Die Hu, Shaoshi Yang, Min Gong, Zhiyong Feng, Xuejun Zhu

Journal Article

UAV search-and-rescue planning using an adaptive memetic algorithm

Libin Hong, Yue Wang, Yichen Du, Xin Chen, Yujun Zheng,yujun.zheng@computer.org

Journal Article

Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

Hongbo REN, Yinlong LU, Qiong WU, Xiu YANG, Aolin ZHOU

Journal Article

Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization

Wan-ying Ruan, Hai-bin Duan,wyruan@buaa.edu.cn,hbduan@buaa.edu.cn

Journal Article

The Development and Research Synopsis of Large UAV in China

Zhao Xu

Journal Article

Secure connectivity analysis in unmanned aerial vehicle networks

Xin YUAN, Zhi-yong FENG, Wen-jun XU, Zhi-qing WEI, Ren-ping LIU

Journal Article

Anewhierarchical software architecture towards safety-critical aspects of a drone system

Xiao-rui ZHU, Chen LIANG, Zhen-guo YIN, Zhong SHAO, Meng-qi LIU, Hao CHEN

Journal Article

Minimax Q-learning design for H∞ control of linear discrete-time systems

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Journal Article

Decentralized fault-tolerant cooperative control of multipleUAVs with prescribed attitude synchronization tracking performance under directed communication topology

Zi-quan YU, Zhi-xiang LIU, You-min ZHANG, Yao-hong QU, Chun-yi SU

Journal Article

A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc

Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang,ch19930611@zju.edu.cn,lvniqi@gmail.com,ghsong@zju.edu.cn,boweiy@zju.edu.cn,jiangxh@zju.edu.cn

Journal Article

A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities

Zhi Zheng, Shuncheng Cai,zhengz@fjnu.edu.cn

Journal Article

Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits

Ayoub SADEGHI, Razieh GHASEMI, Hossein GHASEMIAN, Nabiollah SHIRI,H.ghasemian@sutech.ac.ir

Journal Article

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee

Journal Article