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| 1 | Optimization Algorithms for Predictive Control Approach to Networked Bilinear Systems显示文摘 | Binglin Wang Yu Kang Jiahu Qin Yanmei Li | 2017 | 自动化学报2017，43，7： | 3 |
| 2 | A Fast Approximation Method for Partially Observable Markov Decision Processes显示文摘This paper develops a new lower bound method for POMDPs that approximates the update of a belief by the update of its non-zero states.It uses the underlying MDP to explore the optimal reachable state space from initial belief and select actions during value iterations,which significantly accelerates the convergence speed.Also,an algorithm which collects and prunes belief points based on the upper and lower bounds is presented,and experimental results show that it outperforms some of the state-of-art point-based algorithms. | LIU Bingbing KANG Yu JIANG Xiaofeng QIN Jiahu | 2018 | Journal of Systems Science & Complexity2018，31，6： | 3 |
| 3 | Consensus of multiple second-order vehicles with a time-varying reference signal under directed topology显示文摘 | Jiahu Qin Wei Xing Zheng Huijun Gao | 2011 | Automatica2011，，9： | 1 |
| 4 | FVO: floor vision aided odometry显示文摘In many indoor scenarios, such as restaurants, laboratories, and supermarkets, the planar floors are covered with rectangular tiles. We realized that the abundant parallel lines and crossing points formed by tile joints can be used as natural features to assist indoor localization, and thus we propose a novel indoor localization method for mobile robots by fusing odometry and monocular vision. The method comprises three steps. First, the heading and location of the mobile robot are approximately estimated by odometry based on incremental encoders. Second, with the aid of a camera, the lens of which points vertically toward the floor, the odometric heading estimation can be corrected by detecting the relative angle between the robot's heading and the tile joints. Third, the odometric location estimation is corrected by detecting the perpendicular distance between the image center and the tile joints. As compared with the existing indoor localization methods, the proposed method, called floor vision aided odometry, is not only relatively low in economic cost and computational complexity, but also relatively high in accuracy and robustness. The effectiveness of this method is verified by a real-world experiment based on a differential-drive wheeled mobile robot. | Wenjun LV Yu KANG Jiahu QIN | 2019 | Science China(Information Sciences)2019，62，1： | 1 |
| 5 | Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach显示文摘This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment. | Yanni Wan Jiahu Qin Xinghuo Yu Tao Yang Yu Kang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，1： | 1 |
| 6 | Boundary Gap Based Reactive Navigation in Unknown Environments显示文摘Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing interest.However,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the environment.This paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation method.Specifically,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper gaps.Meanwhile,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local minima.Moreover,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness. | Zhao Gao Jiahu Qin Shuai Wang Yaonan Wang | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，2： | 1 |
| 7 | Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks显示文摘Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient estimates.Traditional stochastic variational inference can only be performed in a centralized manner,which limits its applications in a wide range of situations where data is possessed by multiple nodes.Therefore,this paper develops a novel trust-region based stochastic variational inference algorithm for a general class of conjugate-exponential models over distributed and asynchronous networks,where the global parameters are diffused over the network by using the Metropolis rule and the local parameters are updated by using the trust-region method.Besides,a simple rule is introduced to balance the transmission frequencies between neighboring nodes such that the proposed distributed algorithm can be performed in an asynchronous manner.The utility of the proposed algorithm is tested by fitting the Bernoulli model and the Gaussian model to different datasets on a synthetic network,and experimental results demonstrate its effectiveness and advantages over existing works. | FU Weiming QIN Jiahu LING Qing KANG Yu YE Baijia | 2022 | Journal of Systems Science & Complexity2022，35，6： | 0 |
| 8 | Synchronising second-order multi-agent systems under dynamic topology via reference model-based algorithm显示文摘This paper revisits the synchronisation problem for second-order multi-agent systems(MASs)under dynamically changing communication topology.By employing the reference model-based synchronisation algorithm,it is finally shown that synchronisation for both the position and velocity states can be achieved if the union of the communication topologies has a directed spanning tree frequently enough.This extends the existing results obtained for second-order MASs which exploits mild communication topology condition guaranteeing the synchronisation to a more general case.Convergence analysis is successfully performed by exploiting the product properties of row-stochastic matrices,which can also provide us with an estimate the convergence rate towards the synchronisation. | Jiahu Qin Huijun Gao Tasawar Hayat Fuad E.Alsaadi | 2014 | Journal of Control and Decision2014，1，3： | 0 |
| 9 | Accurate RGB-D SLAM in dynamic environments based on dynamic visual feature removal显示文摘Visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years.However,most proposed visual localization systems assume that the surrounding environment is static,which is difficult to maintain in real-world scenarios due to the presence of moving objects.In this paper,we present DFR-SLAM,a real-time and accurate RGB-D SLAM based on ORB-SLAM2 that achieves satisfactory performance in a variety of challenging dynamic scenarios.At the core of our system lies a motion consensus filtering algorithm estimating the initial camera pose and a graph-cut optimization framework combining long-term observations,prior information,and spatial coherence to jointly distinguish dynamic and static visual features.Other systems for dynamic environments detect dynamic components by using the information from short time-span frames,whereas our system uses observations from a long period of keyframes.We evaluate our system using dynamic sequences from the public TUM dataset,and the evaluation demonstrates that the proposed system outperforms the original ORB-SLAM2 system significantly.In addition,our system provides competitive localization accuracy with satisfactory real-time performance compared to closely related SLAM systems designed to adapt to dynamic environments. | Chenxin LIU Jiahu QIN Shuai WANG Lei YU Yaonan WANG | 2022 | Science China(Information Sciences)2022，65，10： | 0 |
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