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    Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning
    MIAO Zhenhua(苗镇华), HUANG Wentao(黄文焘), ZHANG Yilian(张依恋), FAN Qinqin(范勤勤)
    J Shanghai Jiaotong Univ Sci    2024, 29 (3): 377-387.   DOI: 10.1007/s12204-023-2679-7
    Abstract48)      PDF(pc) (975KB)(30)       Save
    The overall performance of multi-robot collaborative systems is significantly affected by the multirobot task allocation. To improve the effectiveness, robustness, and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper. The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allocation problems. Moreover, a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner. Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm. The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multirobot collaborative systems in uncertain environments, and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
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    Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network
    LIU Zengmin (刘增敏), WANG Shentao(王申涛), YAO Lixiu(姚莉秀), CAI Yunze(蔡云泽)
    J Shanghai Jiaotong Univ Sci    2024, 29 (3): 388-399.   DOI: 10.1007/s12204-022-2540-4
    Abstract24)      PDF(pc) (1105KB)(11)       Save
    In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle (UAV) platform, the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied. Furthermore, a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm. For the problem of object association failure caused by UAV movement, image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm. The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform, and effectively solve the problem of association failure caused by UAV movement.
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    Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction
    CHEN Kun(陈坤), ZHAO Xu(赵旭), DONG Chunyu(董春玉), DI Zichao(邸子超), CHEN Zongzhi(陈宗枝)
    J Shanghai Jiaotong Univ Sci    2024, 29 (3): 400-413.   DOI: 10.1007/s12204-022-2484-8
    Abstract23)      PDF(pc) (5510KB)(12)       Save
    Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion, especially severe occlusion, is an important aspect of evaluating theperformance of object tracking algorithms in long-term tracking, and is of great significance to improving therobustness of object tracking algorithms. However, most object tracking algorithms lack a processing mechanism specifically for occlusion. In the case of occlusion, due to the lack of target information, it is necessary to predict the target position based on the motion trajectory. Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information. A single object tracking method, called probabilistic discriminative model prediction (PrDiMP), is based on the spatial attention mechanism in complex scenes and occlusions. In order to improve the performance of PrDiMP, Kalman filtering, particle filtering and linear filtering are introduced. First, for the occlusion situation, Kalman filtering and particle filtering are respectively introduced to predict the object position, thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model. Second, for detection-jump problem of similar objects in complex scenes, a linear filtering window is added. The evaluation results on the three datasets, including GOT-10k, UAV123 and LaSOT, and the visualization results on several videos, show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed.
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