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Chinese Control and Decision Conference, CCDC

作者： Pengfei Lyu Xiaosheng Yu Chengdong Wu Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Numerous bottom-up salient object detection (SOD) methods rely on local similarity (affinity) to construct, which ignores the relationships among non-adjacent regions. To solve this problem, we propose a novel framework based on absorbing Markov chain (AMC) with cross-level diffusion-based affinity learning, namely CLD. In the AMC model, high transition probability implies reliable relationships among regions. For this purpose, a dense transition probability matrix is learned by a cross-level diffusion strategy. First, to comprehensively reveal the relationship among regions, multi-level features are extracted and are used to calculate multiple initial sparse affinity matrices. Second, the tensor product in the iterative diffusion process has the property of capturing the high-order relationships among features. Thus, by iteratively updating sparse affinity matrices, multiple-level features are effectively fused. Finally, through a weight learning paradigm, outlier data is removed and noise is fully suppressed. Meanwhile, learning weights are also assigned for each sparse affinity matrix and they are integrated into a dense transition probability matrix. Furthermore, to fit salient objects at multiple scales, we propose a refinement model MCLD which is a Bayesian framework under the guidance of high-level features. The MCLD aggregates saliency maps generated by CLD at different scales, and calibrates their shortcomings to produce more stable results. Extensive experiments on five challenging datasets indicate that the proposed method outperforms ten state-of-the-art algorithms.

关键词： Tensors Aggregates Noise Diffusion processes Object detection Feature extraction Bayes methods

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GLMDST: Chinese Dialogue State Tracking Framework Driven by LLM

GLMDST: Chinese Dialogue State Tracking Framework Driven by ...

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Chinese Control and Decision Conference, CCDC

作者： Zixi Jia Bingze Li Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Dialogue State Tracking (DST) is an important component in task-oriented dialogue systems. Accurate dialogue state tracking is essential for generating correct dialogue actions and appropriate natural language responses. However, annotating conversation states is notoriously difficult, leading to a scarcity of DST datasets in languages other than English. With the development of DST, high-quality annotated Chinese conversation datasets have gradually emerged. Despite the emergence of available Chinese conversation datasets, research on the Chinese DST task remains relatively *** emergence of Large Language Model (LLM) such as GPT-3 and ChatGPT has sparked considerable interest in evaluating their effectiveness across different tasks. However, the performance of LLM in Chinese DST remains to be explored. In this study, we introduce GLMDST, a Chinese DST framework powered by a smaller open-source bilingual model called chat-glm3-6b. GLMDST demonstrates outstanding performance in experiments on mainstream Chinese DST benchmarks such as CrossWOZ and RiSAWOZ, showcasing excellent adaptability and generalization capabilities.

关键词： Adaptation models Accuracy Large language models Natural languages Oral communication Benchmark testing Chatbots

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Attention-Based Generative Grasping Deformable Convolutional Neural Network

Attention-Based Generative Grasping Deformable Convolutional...

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International Conference on robotics and Automation engineering (ICRAE)

作者： Kang-Xiong Qin Ru-Jie Fu Sheng-Wei Zhang Yang Jiang Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

Traditional generative grasping convolutional neural network suffers from insufficient feature selection capability and limited adaptability to complex geometric transformations in unstructured environments. To address these issues, this study proposes an optimizational method based on the Convolutional Block Attention Module (CBAM) and Deformable Convolutional Networks (DCN) to improve the performance of Generative Grasping Convolutional Neural Network (GG-CNN) in robotic grasping tasks. The Network is trained and evaluated using the Cornell and Jacquard datasets, the simulation and real-world experiments are conducted. The experimental results show that the AGG-DCNN exhibits better performance in model training and grasping tasks, achieving an accuracy of 97.19% on the Jacquard dataset. Additionally, the network shows significant improvements in convergence speed and robustness in learning accuracy.

关键词： Training Accuracy Deformation Grasping Interference Feature extraction Robustness Surface texture Convolutional neural networks robots

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FlowTrack: Point-level Flow Network for 3D Single Object Tracking

arXiv

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arXiv 2024年

作者： Li, Shuo Cui, Yubo Li, Zhiheng Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang China

3D single object tracking (SOT) is a crucial task in fields of mobile robotics and autonomous driving. Traditional motion-based approaches achieve target tracking by estimating the relative movement of target between two consecutive frames. However, they usually overlook local motion information of the target and fail to exploit historical frame information effectively. To overcome the above limitations, we propose a point-level flow method with multi-frame information for 3D SOT task, called FlowTrack. Specifically, by estimating the flow for each point in the target, our method could capture the local motion details of target, thereby improving the tracking performance. At the same time, to handle scenes with sparse points, we present a learnable target feature as the bridge to efficiently integrate target information from past frames. Moreover, we design a novel Instance Flow Head to transform dense point-level flow into instance-level motion, effectively aggregating local motion information to obtain global target motion. Finally, our method achieves competitive performance with improvements of 5.9% on the KITTI dataset and 2.9% on NuScenes. The code will be made publicly available soon. © 2024, CC BY.

关键词： Target tracking

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Collaborative Path Planning for Multiple Autonomous Underwater Vehicles Based on Improved Particle Swarm Optimization Algorithm

Collaborative Path Planning for Multiple Autonomous Underwat...

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Chinese Control and Decision Conference, CCDC

作者： Mian Wang Hongli Xu Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To solve the collaborative path planning problem of multiple autonomous underwater vehicles in complex environments, an improved particle swarm optimization algorithm is proposed to address the problems of traditional particle swarm optimization algorithms being prone to falling into local optima and having slow convergence speed, resulting in unreasonable search results. Firstly, a dynamic optimization formation transformation obstacle avoidance strategy was adopted for the multi AUV formation problem, and a formation obstacle avoidance strategy was designed that includes multiple modes of formation zero transformation, isomorphic transformation, and heterogeneous transformation. At the same time, this algorithm nonlinearly functionalizes the invariant inertia weights and acceleration constants in traditional particle swarm optimization algorithms, thereby optimizing the optimization ability and solving the problem of traditional particle swarm algorithms being prone to falling into local optima.

关键词： Autonomous underwater vehicles Heuristic algorithms Collaboration Search problems Path planning Particle swarm optimization Vehicle dynamics

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Observation Time Difference: an Online Dynamic Objects Removal Method for Ground Vehicles

Observation Time Difference: an Online Dynamic Objects Remov...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Rongguang Wu Chenglin Pang Xuankang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the process of urban environment mapping, the sequential accumulations of dynamic objects will leave a large number of traces in the map. These traces will usually have bad influences on the localization accuracy and navigation performance of the robot. Therefore, dynamic objects removal plays an important role for creating clean map. However, conventional dynamic objects removal methods usually run offline. That is, the map is reprocessed after it is constructed, which undoubtedly increases additional time costs. To tackle the problem, this paper proposes a novel method for online dynamic objects removal for ground vehicles. According to the observation time difference between the object and the ground where it is located, dynamic objects are classified into two types: suddenly appear and suddenly disappear. For these two kinds of dynamic objects, we propose downward retrieval and upward retrieval methods to eliminate them respectively. We validate our method on SemanticKITTI dataset and author-collected dataset with highly dynamic objects. Compared with other state-of-the-art methods, our method is more efficient and robust, and reduces the running time per frame by more than 60% on average. Our method will be open-sourced on GitHub 1 .

关键词： Location awareness Costs Accuracy Navigation Urban areas Land vehicles Object recognition

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Design and Validation of Pipeline Inspection robot based on Panoramic Vision

Design and Validation of Pipeline Inspection Robot based on ...

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Chinese Control and Decision Conference, CCDC

作者： Zixi Jia Shengming Li Bingze Li Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Pipeline-type workpieces are prevalent in industrial production. However, manually inspecting the inner walls is an inefficient quality control activity that may pose safety risks to inspection personnel, especially for specialized workpieces with internal ramps. The complex internal structure further increases the difficulty of manual inspection. Therefore, designing a robot capable of navigating inside tube-shaped workpieces and stitching images of the inner walls into a single composite image is crucial for improving the efficiency of quality control processes for such workpieces. To meet this demand, we have designed a robot as an experimental platform capable of traveling and climbing ramps within pipes. The robot can handle a maximum climbing angle of 30 degrees and is equipped with four 180-degree wide-angle cameras arranged at 90-degree intervals, facilitating the capture of images around the pipe. Addressing the challenge of narrow environments inside pipes where conventional stitching algorithms may not be effectively applied, we have designed a stitching method capable of seamlessly stitching larger disparity images in narrow pipe environments. This algorithm optimizes the processes of feature point matching and optimal seam line selection, achieving higher-quality image stitching from the four cameras. The robot we designed will significantly enhance the efficiency of inspecting the inner walls of tube-shaped workpieces, particularly those with ramps.

关键词： Service robots Splicing Pipelines robot vision systems Quality control Production Inspection

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Efficient Attentional Underwater Image Enhancement Generative Adversarial Network

Efficient Attentional Underwater Image Enhancement Generativ...

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Chinese Control and Decision Conference, CCDC

作者： Haopeng Zhang Hongli Xu Xiaosheng Yu Junxiang Wang Chengdong Wu Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In the domain of underwater imaging, the attenuation of light, varying light conditions, and suspended particles often degrade image clarity, resulting in a loss of visual information and color distortion. The proposed Efficient Attentional Underwater Image Enhancement Generative Adversarial Network (EAU-GAN) offers a novel approach to address these challenges by leveraging the power of attention mechanisms in deep learning. This model employs an attention-driven generator that adaptively focuses on critical regions in the underwater image, thereby effectively highlighting obscured details while suppressing unnecessary noise. The discriminator, trained adversarially with the generator, ensures the generation of high-quality, naturalistic images. The innovation of EAU-GAN lies in its attention-driven generator that adaptively concentrates on crucial regions within underwater imagery. This targeted focus enhances obscured details and attenuates extraneous noise, markedly improving image clarity and color fidelity. Experimental results show that EAU-GAN outperforms other methods in both qualitative and quantitative aspects.

关键词： Technological innovation Visualization Image color analysis Image synthesis Imaging Generative adversarial networks Distortion

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Research on Continuous Control Approach of Mobile robots for Mapless Navigation 14

Research on Continuous Control Approach of Mobile Robots for...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zou, Qiang Liu, Jingyuan Cong, Ming Liu, Dong Wang, Guiyuan Northeastern University Faculty of Robot Science and Engineering Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China College of Information Science and Engineering Northeast University Shenyang China School of Mechanical Engineering Dalian University of Technology Dalian China Jiangsu Shuguang Optoelectronics Co. Ltd. Yangzhou China

ISBN: (纸本)9798331506056

Mapless navigation is a widely-used technique for directing mobile robots through an unknown environment. Inspired by goal-oriented navigation features of mammals in nature, this work introduces heuristic knowledge and proposes a method known as Heuristic Knowledge based Deep Deterministic Policy Gradient, HK-DDPG, to direct the robot's motion actions, which can reduce the randomness of actions effectively. To solve the issues of low sample date utilization and poor training stability in DDPG, HK-DDPG incorporates a temporal difference error and a weight-based optimization sampling mechanism. The results of simulation and real environment evaluation experiments demonstrate that HK-DDPG algorithm exhibits faster convergence, higher learning efficiency, and better stability than DDPG algorithm. The robot deployed with the proposed model can be extended for application in real-world scenarios. © 2024 IEEE.

关键词： Mobile robots

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Experimental and numerical simulation on the sliding process of roof rock blocks triggered by dynamic disturbance

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Underground Space 2023年第3期10卷 269-293页

作者： Feng Dai Wancheng Zhu Min Ren Leilei Niu Chen Hou Center for Rock Instability and Seismicity Research Department of Mining EngineeringSchool of Resources and Civil EngineeringNortheastern UniversityShenyang 110819China Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunming 650093China

With the excavation of underground opening,the roof rock block may fall under external dynamic *** this paper,the roof rock mass was simplified as a rock block system composed of trapezoidal rock blocks.A series of experiments and numerical simulations were performed to study the sliding process of the key block under the horizontal static clamping load and vertical impact *** propagation of stress wave in the block system were captured and analyzed by using high-speed camera and digital image correlation *** results reveal that a pendulum-type wave was generated due to the propagation and superposition of stress waves in the block ***,the governing mechanism of the sliding displacement of the key block was clarified based on the propagation of incident stress waves or pendulum-type ***,it is found that the sliding distance of the key block decreases in a power function with the increasing friction coefficient,or decreases in a parabolic function with the increasing trapezoid internal ***,a case study on the roof block sliding of the roadway at a gold mine was conducted,and it is concluded that the sliding of the key block resulted from the coupled effects of"shear driving"and"low friction"driven by stress wave propagation,regardless of a single or multi-layer rock block *** results may provide technical guideline for preventing rock-falling accidents caused by blasting distur-bances in underground mining.

关键词： Roof rock block Sliding instability Dynamic disturbance Pendulum-type wave Digital image correlation technique Numerical simulation

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