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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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Accurately Detecting Dense Small Objects from Aerial Images by Fusing Local Enhanced Features and Balanced Category Loss

Accurately Detecting Dense Small Objects from Aerial Images ...

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Chinese Control and Decision Conference, CCDC

作者： Xinhui Zhang Qichuan Ding Yilong Dou Shuai Ren College of Information Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Object detection from aerial images has been successfully used in intelligent transportation systems and disaster relief. Due to the large ranges of aerial images, disordered scenes, and various environments, accurately detecting valuable objects from aerial images is difficult. Current "You Only Look Once" detection algorithms commonly suffer from the problems of missed/false detection and imbalanced category detection, especially when detecting small objects. Thus, an improved network based on YOLOv5, named YOLOv5-EPD, has been developed to address the above problems. First, the adaptive up-sampling operator was embedded into the up-down path of PANet to enhance its ability in extracting local semantic features. Then, a large-size convolutional kernel was inserted into the down-up path of PANet to extend its receptive field. Therefore, the YOLOv5 network involving the improved PANet module is capable of obtaining richer semantic and spatial information on small objects. Furthermore, a dynamic cross-entropy loss has been developed to adjust the percentage of each category classification loss, to balance the detection accuracies of different categories. Experiments on a public dataset (VisDrone2019) showed that YOLOv5-EPD was superior to the existing detection methods, improving by 3.8% of mAP50 compared with the basic YOLOv5, which reveals the application potential of our approach in aerial images object detection.

关键词： YOLO Accuracy Disasters Semantics Feature extraction Kernel Detection algorithms

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Multi-UAV Cooperative Fixed-point Detection Method Based on Conflict Search

Multi-UAV Cooperative Fixed-point Detection Method Based on ...

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Chinese Control and Decision Conference, CCDC

作者： Jingyu Ru Jiayu Liu Zhongcheng Wang Yifan Gao Hongli Xu Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

A cooperative fixed-point detection algorithm for multiple UAVs based on conflict search is proposed, which solves the problem of continuous cooperative detection of a certain place by multiple UAVs. This method can monitor and resolve the conflict in real time by setting the conflict between UAVs with large repetition times of detection time, so that multiple UAVs can continuously detect a certain place. At the same time, some improvements are made to the algorithm, and the detection time of UAV is prolonged by adding detection time to heuristic function; In order to meet the high real-time requirements of multi-UAV system, FOCAL-list is added to improve the running speed of the algorithm. Experimental results show that the proposed algorithm can achieve multi-UAV cooperative fixed-point detection, compared with the swarm intelligence algorithm, the improved algorithm has better effect.

关键词： Heuristic algorithms Autonomous aerial vehicles Search problems Real-time systems Path planning Mathematical models Particle swarm optimization

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Decision Support for Beyond Visual Range Air Combat Based on HTN Planning and Machine Learning

Decision Support for Beyond Visual Range Air Combat Based on...

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Chinese Control and Decision Conference, CCDC

作者： Jiaqi Wang Hongda Li Jingyu Ru Hongli Xu Faculty of Robot Science and Engineering Northeastern University Shenyang China Colloge of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Beyond visual range air combat (BVR) has become the main form of modern air combat. In order to solve the problem of pilot information overload in BVR air combat, this paper designs an assistance decision-making model based on Hierarchical Task Network (HTN) planning and machine learning algorithms to provide pilots with tactical strategies. This paper first designs a hierarchical task network in a BVR air combat scenario, as well as an efficient scoring system that can dynamically adjust the mission execution plan based on the situation in real time. Then a machine learning algorithm combined with data generalization was proposed to predict the launch effectiveness of missiles in different situations and optimize part of the scoring process. Finally, a human-in-the-loop confrontation simulation experiment was conducted, and the feasibility of the model and the effectiveness of the algorithm were verified by analyzing the winning rate.

关键词： Missiles Visualization Machine learning algorithms Atmospheric modeling Decision making Real-time systems Human in the loop

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A Reinforcement Learning-based Approach to Improve the Trajectory Generalization Performance of DMP

A Reinforcement Learning-based Approach to Improve the Traje...

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第42届中国控制会议

作者： Peng Yu Lijin Fang Yian Qian College of Information Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University

Dynamic Motion Primitives(DMP) is a trajectory coding method based on imitation learning, which achieves a wide application owing to its robustness and generalization ability. However, the generalization of DMP in some special cases is not very satisfactory. For instance, when the target point changes, the similarity of the middle part of the trajectory is also greatly affected. Moreover, scaling the trajectory of the DMP is often followed by a reduction in endpoint accuracy. To solve the above problems, the following work is carried out in this paper: First, we reviewed the basic DMP framework and analyzed its worthy improvements. Second, the Pearson correlation coefficient is introduced to quantitatively assess the similarity of generalized trajectories. Next, a model-free, sampling-based reinforcement learning framework Path Integrals(PI2) is designed for the optimization of generalized trajectories, where the Gaussian weights of the DMP are iteratively updated ***, the feasibility of the method in this paper is verified by simulation in the case of target variation and trajectory scaling.

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A Lightweight and Dynamic Convolutional Network for Real-time Semantic Segmentation

A Lightweight and Dynamic Convolutional Network for Real-tim...

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第35届中国控制与决策会议

作者： Chunyu Zhang Fang Xu Chengdong Wu Faculty of Robot Science and Engineering Northeastern University Shenyang Siasun Robot & Automation Company Ltd

Semantic segmentation is a difficult task that satisfies most of the demands of autonomous driving and drone aerial photography in a unified *** neural networks can properly categorize image pixels via end-to-end model ***,achieving the optimal trade-off between segmentation precision and the number of network parameters while maintaining a suitable inference time has become a challenging *** this paper,we propose a lightweight dynamic convolutional semantic segmentation network,LDCNet,which belongs to the asymmetric network ***,we designed a coding module that includes dynamic convolution:*** success of this module is attributed to the use of dynamic convolution,which increases the utilization of local and contextual information of the *** also designed the decoding module containing feature pyramids and hybrid attention:HA-FP,which performs a multi-scale fusion of features accompanied by feature *** the Cityscapes and Camvid datasets,LDCNet obtains 73.5 mIoU and 69.4 mIoU accuracy with 78.4 FPS and91.3 FPS,respectively,without pre-training or *** experimental findings reveal that LDCNet achieves an outstanding balance between segmentation accuracy and network parameters with just 0.96 M.

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A Resource Scheduling Optimization Algorithm of Self-organizing Cloud robot Platform

A Resource Scheduling Optimization Algorithm of Self-organiz...

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第35届中国控制与决策会议

作者： Ji Peng Gao Wei Li Ming Li Xiaoqiang Faculty of Robot Science and Engineering Northeastern University College of Information Science and Engineering Northeastern University

It is a research hotspot in the field of robotics to improve the running efficiency of robots based on the powerful storage and computing resources of the cloud ***,the traditional cloud computing technology is too centralized to adapt to the military,rescue,and other complex environments,so the self-organizing cloud robot technology that can achieve resource sharing and collaborative computing has received more and more *** self-organizing cloud robot has the characteristics of high stability,strong robustness,fast running speed,and strong computing *** meet the requirements of self-organizing cloud robots for cooperative work and shared computing,a resource optimization scheduling algorithm for a self-organizing robot platform is proposed in this *** the algorithm,a self-organizing network model is built based on the Raft algorithm to realize the interaction and sharing of computing resources between *** this basis,an optimization model for computing resource scheduling under time constraints that are based on the DQN algorithm is *** construction of the reward function and the design of the neural network model are used to achieve effective resource allocation and scheduling of self-organizing cloud robot systems for different task *** evaluate the performance of the proposed algorithm the simulation experiments are *** results show the proposed algorithm is more effective in resource scheduling compared with other current algorithms.

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AI-Based Maize and Weeds Detection on the Edge with CornWeed Dataset 18

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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18th Conference on Computer science and Intelligence Systems, FedCSIS 2023

作者： Iqbal, Naeem Manss, Christoph Scholz, Christian Konig, Daniel Igelbrink, Matthias Ruckelshausen, Arno Dfki Plan-based Robot Control Osnabrueck Germany Dfki Marine Perception Oldenburg Germany University of Applied Sciences Osnabrueck Faculty of Engineering and Computer Science Osnabrueck Germany

ISBN: (纸本)9788396744784

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange. © 2023 Polish Information Processing Society.

关键词： Data acquisition

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Subconscious robotic Imitation Learning

arXiv

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arXiv 2024年

作者： Xie, Jun Wang, Zhicheng Tan, Jianwei Lin, Huanxu Ma, Xiaoguang Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School Northeastern University Foshan528311 China

Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions, resulting in slow execution and limited real-time responsiveness. Instead, human beings subconscious can constantly process and store vast amounts of information from their experiences, perceptions, and learning, allowing them to fulfill complex actions such as riding a bike, without consciously thinking about each. Inspired by this phenomenon in action neurology, we introduced subconscious robotic imitation learning (SRIL), wherein cognitive offloading was combined with historical action chunkings to reduce delays caused by model inferences, thereby accelerating task execution. This process was further enhanced by subconscious downsampling and pattern augmented learning policy wherein intent-rich information was addressed with quantized sampling techniques to improve manipulation efficiency. Experimental results demonstrated that execution speeds of the SRIL were 100% to 200% faster over SOTA policies for comprehensive dual-arm tasks, with consistently higher success rates. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Human Target Detection and Tracking Algorithm Based on Improved YOLOv7

Human Target Detection and Tracking Algorithm Based on Impro...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Yuyao Min Jiayi Shui Shiyu Liu Junyi Wang Zexin Guo Department of Faculty of Robot Science and Engineering Northeastern University Shenyang China

Recently, human-oriented target tracking has broad application prospects in various practical fields, such as medicine and security. YOLOv7 is a powerful detection algorithm in the current main-stream, and it still has room for development in terms of detection accuracy. To handle the issue of mobile robots struggling to maintain recognition precision and stability in complicated scenes, we propose an improved target detection network for YOLOv7. Firstly, based on YOLOv7-tiny network structure, the CBAM attention mechanism is introduced into the bottom of the backbone network, and the SPPCSP module is adopted to reduce the computation of the feature map, which could enhance the network's representational capability. Then, the KCF algorithm is incorporated into the model, which could track the human target after determining its position with respect to the robot with the RGB-D camera. In order to ensure the flexibility of the robot, the Jetson Nano is utilized as the processor. Finally, we conduct human target tracking experiments for the mobile robot in a realistic scenario. The experimental results show that the mAP reaches 91.17%, which is 1.88% higher than only using YOLOv7 after introducing the CBAM module.

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