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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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Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles Research Article

Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1020-1031 doi: 10.1631/FITEE.2100559

Abstract: We investigate a for formations with external disturbances and obstacles. The strategy is based on a framework and . First, we propose a to estimate the influence of a disturbance, and prove that the observer converges in fixed time using a Lyapunov function. Second, we design an based on topology reconstruction, by which the UAV can save energy and safely pass obstacles. Third, we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors. Further, the cost function of each UAV is designed, by which the UAV formation problem is transformed into a game problem. Finally, we develop LFPIO and use it to solve the Nash equilibrium. Numerical simulations are conducted, and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.

Keywords： Distributed game strategy     Unmanned aerial vehicle (UAV)     Distributed model predictive control (MPC)     Levy flight based pigeon inspired optimization (LFPIO)     Non-singular fast terminal sliding mode observer (NFTSMO)     Obstacle avoidance strategy    

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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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An Intelligent System for Navigation Collision Prevention

Hao Yanling,Liu Yuhong,Sun Feng,Sun Yao

Strategic Study of CAE 2000, Volume 2, Issue 3,   Pages 48-53

Abstract:

The purpose of this thesis is to developing and exploiting an intelligent system for collision prevention, namely “Intelligent Collision Prevention Expert System for Navigation”(NICPES). The NICPES has a multi-unit and layering Knowledge Base systematic structure and a multi-unit Knowledge Representation (KR) which based on frame KR, production rule KR, procedure KR and neural network KR, to represent and store all kinds of knowledge for navigation collision prevention. The NICPES also builds a multi-inference system, which based on analogy inference, forward illation inference, conversion inference, neural network inference and meta-rule inference, to overcome the shortcoming of unitary inference. For-some problems in collision prevention region, the NICPES builds a set of models to solve them. These models comprise the models of judging collision risk, the model of determining collision prevention time and the model of classifying encounter situation. For multi-ship encounter situation, the NICPES puts forward a tactics to choose optimal collision prevention scheme based on Analytic Hierarchy Process (AHP) and builds a mathematical model that will be used to determine the optimal angle and sailing time during ship's turning for multi and single ship encounter situation. The simulation experiments show that the NICPES can analyze and judge various sailing cases and encounter situation ,and offer a reasonable scheme, which settle the collision problem effectively and ensure the sailing safety.

Keywords： collision prevention     expert system     neural network     fuzzy technique     multi-target optimizing    

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A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Strategic Study of CAE 2012, Volume 14, Issue 11,   Pages 107-112

Abstract:

The optimal equilibrium between the multiple objectives of construction projects is a significant aspect of project management research, which has seen rapid development in recent years, gaining a bunch of fruitful achievements. In this paper, a review is provided for the multiobjective tradeoff research of construction projects based on literature review. Models under deterministic conditions and nondeterministic conditions are investigated and summarized. Some suggestions on the possible direction for future research are included considering the algorithms adopted in the problem solution. This paper aims at providing a review of the achievements in this area so far and keeping track of the ongoing research topics so as to give certain indications for research that follows.

Keywords： construction project     project management     multi-objective optimization     intelligent optimization algorithm    

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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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Evolutionary Algorithms for Multi-objective Optimization and Decision-Making Problems

Xie Tao Chen Huowang

Strategic Study of CAE 2002, Volume 4, Issue 2,   Pages 59-68

Abstract:

Multi-objective optimization (MOO) and decision-making (DM) has become an important research area of evolutionary computations in recent years. The researches on multi-objective evolutionary algorithms (MOEA) focus mainly on the Pareto-based comparison and ordering of individuals, fitness assignment and Riching techniques, etc., so that the population can converge and uniformly distribute in the Pareto front. This paper presents an introduction to the history and classification of multi-objective optimization and decision-making techniques, analyzes both the Pareto-based and non-Pareto-based evolutionary algorithms, and,particularly,the five well-known MOEAs. Some problems related to the researches on MOEAs are addressed in details, such as the characteristics of Pareto front, the test suite and performance evaluation of MOEAs, the MOEA convergence analysis, the MOEA parallelization, and the disposal of real world MOO problems.

Keywords： evolutionary algorithms     multi-objective optimization and decision-making     Pareto optimal    

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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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Network Planning Multi-objective Optimization Based on Differential Evolution

Li Gaoyang,Wu Yuhua,Liu Mingguang

Strategic Study of CAE 2006, Volume 8, Issue 6,   Pages 60-63

Abstract:

Based on the synthetic study of costing, quality and construction period, the multi-objective optimal model considering the maximal pure value and quality of construction project is made in order to improve economic benefit of construction enterprise. Then, a new method called differential evolution is introduced into the multi-objective optimization. The model and validity of this algorithm are tested through project case.

Keywords： network planning     multi-objective optimization     differential evolution     net profit     quality    

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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: In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topology. This may lead to UANET service performance issues. In this study, for planning rapidly changing UAV swarms, we propose a dynamic value iteration network (DVIN) model trained using the method with the connection information of UANETs to generate a state value spread function, which enables UAVs/nodes to adapt to novel physical locations. We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method. Simulation results demonstrate that the proposed model significantly reduces the decision-making time for UAV/node with a high average success rate.

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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Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Strategic Study of CAE 2000, Volume 2, Issue 9,   Pages 65-69

Abstract:

Based on the concepts of the fuzzy weighted distance and membership degree, the fuzzy optimum selection dynamic programming technique that can be used for the optimization of multi-objective and multi-stage systems are developed by means of the combination of fuzzy optimum selection theory with dynamic programming technique. This is a new methodology for solving the multi-objective optimization problems of multi-stage systems. Finally, an application to the optimization of a multiple reactor system is given as an example.

Keywords： multi-stage     multi-objective optimization     fuzzy optimum selection     dynamic programming     membership degree    

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Affine formation tracking control of unmanned aerial vehicles Research Articles

Huiming LI, Hao CHEN, Xiangke WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 909-919 doi: 10.1631/FITEE.2100109

Abstract:

The tracking problem for (UAVs) is considered in this paper, where fixed-wing UAVs are modeled as unicycle-type agents with asymmetrical speed constraints. A group of UAVs are required to generate and track a time-varying target formation obtained by affinely transforming a nominal formation. To handle this problem, a distributed control law based on stress matrix is proposed under the leader-follower control scheme. It is proved, theoretically, that followers can converge to the desired positions and achieve affine transformations while tracking diverse trajectories. Furthermore, a saturated control strategy is proposed to meet the speed constraints of fixed-wing UAVs, and numerical simulations are executed to verify the effectiveness of our proposed tracking control strategy in improving maneuverability.

Keywords： Affine formation     Fixed-wing unmanned aerial vehicles     Multi-agent system    

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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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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization Research Article

Jian DONG, Xia YUAN, Meng WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1390-1406 doi: 10.1631/FITEE.2100420

Abstract: We propose a competitive binary (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.

Keywords： Antenna topology optimization     Multi-objective grey wolf optimizer     High-dimensional mixed variables     Fast design    

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A Multi-objective Optimization Decision-making Model for Project Time - resource Tradeoff Problem

Wang Xianjia,Wan Zhongping

Strategic Study of CAE 2005, Volume 7, Issue 2,   Pages 35-40

Abstract:

In the project scheduling and management, the time-resource tradeoff problem is to seek the objective of minimizing the project duration and the total consumed-resources cost under the requirement of the absolute due date of project, and determine an efficient project scheduling according to some precedence relationship and the renewable resource constraints. A new multi-objective optimization decision-making model with time-resource tradeoff problem is proposed, in which objective functions with conflict one another are defined as adaptive and adjustable between the project duration and the total consumed-resources cost in all period. A satisfied feasible solution can be obtained in the solution procedure by compromising and adjusting relationship between the project duration and the total consumed-resource cost. A numerical example is illustrated. In addition, some characteristics on this two-player game are given in the corresponding Lagrangian relaxation form associated with the resource constraints.

Keywords： project scheduling management     time-resource tradeoff     multiobject decision-making model     resource-constrained     project scheduling     Lagragian relaxation    

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Title Author Date Type Operation

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

Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles

Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN

Journal Article

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

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

Journal Article

An Intelligent System for Navigation Collision Prevention

Hao Yanling,Liu Yuhong,Sun Feng,Sun Yao

Journal Article

A review of the multiobjective tradeoff research of construction projects based on intelligent optimization algorithm

Zhang Lianying,Xu Chang,Wu Qiong

Journal Article

The Development and Research Synopsis of Large UAV in China

Zhao Xu

Journal Article

Evolutionary Algorithms for Multi-objective Optimization and Decision-Making Problems

Xie Tao Chen Huowang

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

Network Planning Multi-objective Optimization Based on Differential Evolution

Li Gaoyang,Wu Yuhua,Liu Mingguang

Journal Article

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

Fuzzy Optimum Selection Dynamic Programming Methodology for Multi-objective Optimization of Multi-stage Systems

Xiong Deqi,Yin Peihai

Journal Article

Affine formation tracking control of unmanned aerial vehicles

Huiming LI, Hao CHEN, Xiangke WANG

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

Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Jian DONG, Xia YUAN, Meng WANG

Journal Article

A Multi-objective Optimization Decision-making Model for Project Time - resource Tradeoff Problem

Wang Xianjia,Wan Zhongping

Journal Article