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Path Planning Algorithm for Unmanned Surface Vessel Based on Multiobjective Reinforcement Learning

Computational intelligence and neuroscience 2023年第1期2023卷 2146314页

作者： Caipei Yang Yingqi Zhao Xuan Cai Wei Wei Xingxing Feng Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. Wuhan Second Ship Design and Research Institute Wuhan 430205 China.

It is challenging to perform path planning tasks in complex marine environments as the unmanned surface vessel approaches the goal while avoiding obstacles. However, the conflict between the two subtarget tasks of obstacle avoidance and goal approaching makes the path planning difficult. Thus, a path planning method for unmanned surface vessel based on multiobjective reinforcement learning is proposed under the complex environment with high randomness and multiple dynamic obstacles. Firstly, the path planning scene is set as the main scene, and the two subtarget scenes including obstacle avoidance and goal approaching are divided from it. The action selection strategy in each subtarget scene is trained through the double deep -network with prioritized experience replay. A multiobjective reinforcement learning framework based on ensemble learning is further designed for policy integration in the main scene. Finally, by selecting the strategy from subtarget scenes in the designed framework, an optimized action selection strategy is trained and used for the action decision of the agent in the main scene. Compared with traditional value-based reinforcement learning methods, the proposed method achieves a 93% success rate in path planning in simulation scenes. Furthermore, the average length of the paths planned by the proposed method is 3.28% and 1.97% shorter than that of PER-DDQN and dueling DQN, respectively.

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Integrated optimization of Order Batching and Distribution based on EDA

Integrated optimization of Order Batching and Distribution b...

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第32届中国控制与决策会议

作者： Shunli Feng Yindong Shen Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of science and technology

The order picking and distribution are two necessary activities in logistics,the cost of which accounts for a considerable portion of the logistics *** there are dependencies between these two activities,a lot of work has been carried out on them independently;hence,the optimization may be *** paper proposes an integrated order batching and distribution *** objectives are to minimize the makespan（including picking time and delivery time） and to minimize the number of delivery *** solve the integrated model,an EDA approach is devised,in which an elaborate encoding/decoding method is employed and an incremental learning probability model is *** show that the proposed integrated model and its solution approach can lead to a significant reduction in the total cost（weighted sum of the makespan and the number of delivery trips）.

关键词： Order Batching Order picking Logistics Distribution EDA approach

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Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

Research Square

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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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Vision-based Autonomous Landing of Unmanned Aerial Vehicle on a Motional Unmanned Surface Vessel

Vision-based Autonomous Landing of Unmanned Aerial Vehicle o...

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第三十九届中国控制会议

作者： Zhe-Cheng Xu Bin-Bin Hu Bin Liu XD Wang Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and Technology

This paper has developed a vision-based control approach for autonomous tracking and landing on a motional Unmanned surface vessel（USV） with an unmanned aerial vehicle（UAV）. First, a novel three-stage visual detection method is proposed to estimate the relative 3 D position between UAV and USV, where the USV stays in a suitable view of the camera of the UAV. Then a PID controller is designed to regulate the attitude of the quadrotor and the movement of the vessel, which enables the successful landing procedure of the UAV on a motional USV. Finally, lake experiments are conducted to verify the effectiveness of the proposed algorithm.

关键词： Autonomous landing Unmanned aerial vehicle Unmanned surface vessel Computer vision

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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A mini-review of noble-metal-free electrocatalysts for overall water splitting in non-alkaline electrolytes

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Materials Reports(Energy) 2021年第2期1卷 48-60页

作者： Jie Yu Yawen Dai Qijiao He Dongqi Zhao Zongping Shao Meng Ni Department of Building and Real Estate The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong999077PR China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and TechnologyWuhan430074PR China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing210009PR China Department of Chemical Engineering Curtin UniversityPerthWestern Australia6845Australia Environmental Energy Research Group Research Institute for Sustainable Urban Development(RISUD)The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong999077PR China

Development of noble-metal-free materials with remarkable electrocatalytic water-splitting performance in acidic or neutral media has sparked considerable attention in recent ***,we review the latest research on design and fabrication of precious-metal-free catalytic materials for overall water electrolysis in non-alkaline environment,especially highlighting several optimizing approaches to enhance the catalytic behavior and to realize effective bifunctional *** these involved noble-metal-free electrocatalysts are classified into transition-metal oxides(TMOs),transition-metal nitrides(TMNs),transition-metal carbides(TMCs),transition-metal phosphides(TMPs),transition-metal chalcogenides,metal complexes,and metal-free carbons,as shown in the main ***,the paper also offers an introduction of the fundamental electrochemistry of water splitting before entering the subject,as well as a prospective discussion on mechanism understanding,novel catalysts fabrication,and standardized performance measurements/evaluation in the last section.

关键词： Noble-metal-free electrocatalyst Overall water splitting Oxygen evolution reaction Hydrogen evolution reaction Non-alkaline electrolyte Bifunctional catalytic electrode

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Temporal context aggregation network for temporal action proposal refinement

arXiv

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arXiv 2021年

作者： Qing, Zhiwu Su, Haisheng Gan, Weihao Wang, Dongliang Wu, Wei Wang, Xiang Qiao, Yu Yan, Junjie Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China SenseTime Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation

Large-scale Multiobjective Optimization via Problem Decompos...

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Congress on Evolutionary Computation

作者： Lianghao Li Cheng He Ran Cheng Linqiang Pan Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Large-scale multiobjective optimization problems (LSMOPs) are challenging for existing approaches due to the complexity of objective functions and the massive volume of decision space. Some large-scale multiobjective evolutionary algorithms (LSmoeAs) have recently been proposed, which have shown their effectiveness in solving some benchmarks and real-world applications. They merely focus on handling the massive volume of decision space and ignore the complexity of LSMOPs in terms of objective functions. The complexity issue is also important since the complexity grows along with the increment in the number of decision variables. Our previous study proposed a framework to accelerate evolutionary large-scale multiobjective optimization via problem reformulation for handling large-scale decision variables. Here, we investigate the effectiveness of LSMOF combined with decomposition-based moeA (moeA/D), aiming to handle the complexity of LSMOPs in both the decision and objective spaces. Specifically, moeA/D is embedded in LSMOF via two different strategies, and the proposed algorithm is tested on various benchmark LSMOPs. Experimental results indicate the encouraging performance improvement benefited from the solution of the complexity issue in large-scale multiobjective optimization.

关键词： Sociology Evolutionary computation Benchmark testing Linear programming Complexity theory Statistics Optimization

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Down-Level control Based on Level Prediction of Landslide Evolutionary State

Down-Level Control Based on Level Prediction of Landslide Ev...

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International Conference on Information Science and Technology (ICIST)

作者： Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Accurate prediction and effective control are the key to reducing the impact of landslide disasters. In practice, most of recent studies focus on landslide displacement prediction and control. In this paper, we propose a new control method based on the level prediction of landslide evolution state, namely down-level control. The core components are level predictor and interval predictor in this method. Specially, we first transform the traditional displacement regression prediction problem into a level classification prediction problem by labeling discrete category information for landslide sample points. Then the level predictor based on Multi-task learning-Stacked long-short time memory network (MTL-SLSTM) is established to predict the state of the landslide and judge whether the system needs to be controlled. Finally, we design a safe rainfall interval predictor based on bootstrap method to obtain the safe value of control variation. The effectiveness of the proposed control method is verified on Baishuihe and Shiliushubao landslides. The results show the proposed down-level control method is valid and more intuitive.

关键词： Information science Geology Transforms Predictive models Terrain factors Complexity theory Labeling

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Stability of partially unstable discrete-time switched positive nonlinear systems with delays

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Applied Mathematics and Computation 2025年 507卷

作者： Jinyuan Ni Yin Sheng Qiang Xiao Zhigang Zeng Tingwen Huang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example.

关键词： Exponential stability Switched positive nonlinear systems Time delay Unstable subsystems

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