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NAMR-RRT: Neural Adaptive Motion Planning for Mobile Robots in Dynamic Environments

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arXiv 2024年

作者： Sun, Zhirui Xia, Bingyi Xie, Peijia Li, Xiaoxiao Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Peng Cheng National Laboratory Shenzhen China The Harbin Institute of Technology Shenzhen Shenzhen China

Robots are increasingly deployed in dynamic and crowded environments, such as urban areas and shopping malls, where efficient and robust navigation is crucial. Traditional risk-based motion planning algorithms face challenges in such scenarios due to the lack of a well-defined search region, leading to inefficient exploration in irrelevant areas. While bi-directional and multi-directional search strategies can improve efficiency, they still result in significant unnecessary exploration. This article introduces the Neural Adaptive Multi-directional Risk-based Rapidly-exploring Random Tree (NAMR-RRT) to address these limitations. NAMR-RRT integrates neural network-generated heuristic regions to dynamically guide the exploration process, continuously refining the heuristic region and sampling rates during the planning process. This adaptive feature significantly enhances performance compared to neural-based methods with fixed heuristic regions and sampling rates. NAMR-RRT improves planning efficiency, reduces trajectory length, and ensures higher success by focusing the search on promising areas and continuously adjusting to environments. The experiment results from both simulations and real-world applications demonstrate the robustness and effectiveness of our proposed method in navigating dynamic environments. A website about this work is available at https://***/view/namr-rrt. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

Functional Connectivity Analysis Revealed Abnormal Brain Network Pattern in Parkinson's Disease with Freezing of Gait

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Functional Connectivity Analysis Revealed Abnormal Brain Net...

第42届中国控制会议

作者： Zhilin Shu Jinrui Liu Ting Song Jianda Han Ningbo Yu College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

Patients with Parkinson's disease(PD) commonly have deficits in lower-limb *** of gait(FOG),a type of gait disturbance of PD,is a complex motor symptom exhibited as inability to generate an effective *** mechanism of lower-limb dysfunction of PD,especially FOG,remains to be *** investigate the neural mechanisms related to FOG in PD,we evaluated 39 subjects(13 PD patients with FOG,13 PD patients without FOG,and 13 healthy subjects) based on electroencephalogram(EEG) signals while they performed the pedaling *** conducted functional connectivity analysis using Magnitude Squared Coherence(MSC) and calculated the metrics to investigate the characteristics of the brain network among the three *** results indicated that PD patients were associated with reduced functional integration and segregation of brain network,subserving both motor and cognitive *** reduced functional integration and segregation of cognitive processes were correlated with the severity of *** study revealed the differences in brain networks between PD patients and healthy controls and provided insights into the neural mechanism of FOG.

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HPPS: A Hierarchical Progressive perception System for Luggage Trolley Detection and Localization at Airports

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

Efficient Multi-agent Navigation with Lightweight DRL Policy

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arXiv 2024年

作者： Diao, Xingrong Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing314000 China

This article presents an end-to-end collision avoidance policy based on deep reinforcement learning (DRL) for multi-agent systems, demonstrating encouraging outcomes in real-world applications. In particular, our policy calculates the control commands of the agent based on the raw LiDAR observations. In addition, the number of parameters of the proposed basic model is 140,000, and the size of the parameter file is 3.5 MB, which allows the robot to calculate the actions from the CPU alone. We propose a multi-agent training platform based on a physics-enhanced simulator to further bridge the gap between simulation and the real world. The policy is trained on a policy-gradients-based RL algorithm in a dense and messy training environment. A novel reward function is introduced to address the issue of agents choosing suboptimal actions in some common scenarios. Although the data used for training is exclusively from the simulation platform, the policy can be successfully transferred and deployed in real-world robots. Finally, our policy effectively responds to intentional obstructions and avoids collisions. The website is aviliable in https://***/view/ xingrong2024efficient/%E9%A6%96%E9%A1%B5 Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

AdaFlow: Efficient Long Video Editing via Adaptive Attention Slimming And keyframe Selection

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arXiv 2025年

作者： Zhang, Shuheng Liu, Yuqi Zhou, Hongbo Peng, Jun Zhou, Yiyi Sun, Xiaoshuai Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China Institute of Artificial Intelligence Xiamen University 361005 China Peng Cheng Laboratory Shenzhen518000 China

Despite great progress, text-driven long video editing is still notoriously challenging mainly due to excessive memory overhead. Although recent efforts have simplified this task into a two-step process of keyframe translation and interpolation generation, the token-wise keyframe translation still plagues the upper limit of video length. In this paper, we propose a novel and training-free approach towards efficient and effective long video editing, termed AdaFlow. We first reveal that not all tokens of video frames hold equal importance for keyframe translation, based on which we propose an Adaptive Attention Slimming scheme for AdaFlow to squeeze the KV sequence, thus increasing the number of keyframes for translations by an order of magnitude. In addition, an Adaptive keyframe Selection scheme is also equipped to select the representative frames for joint editing, further improving generation quality. With these innovative designs, AdaFlow achieves high-quality long video editing of minutes in one inference, i.e., more than 1k frames on one A800 GPU, which is about ten times longer than the compared methods, e.g., TokenFlow. To validate AdaFlow, we also build a new benchmark for long video editing with high-quality annotations, termed LongV-EVAL. Our code is released at: https://***/jidantang55/AdaFlow. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

Leveraging Three Orthogonal Magnetic Field Sources for Localization of A Magnetic Capsule Endoscope with Unique Solution

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Leveraging Three Orthogonal Magnetic Field Sources for Local...

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, keyu Xu, Yangxin Zhao, Ziqi Meng, Max Q.-H. The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Yuanhua Robotics Perception & AI Technologies Limited Shenzhen518000 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Shenzhen Research Institute The Chinese University of HongKong Shenzhen518057 China

ISBN: (纸本)9781665481090

This paper presents a novel localization method for magnetically actuated capsule endoscopy that uses three orthogonal magnetic field sources external to the capsule, and three magnetic field sensors and an inertial measurement unit (IMU) internal to the capsule to estimate the 6-DOF pose of the capsule with unique solution. One permanent magnet and two electromagnetic coils are used as the external magnetic field sources to generate static and time-varying magnetic fields with different frequencies, and their measurements from raw sensor readings are separated and extracted based on the Fast Fourier Transform (FFT) algorithm. Furthermore, we analyse the issue of multiple solutions that may occur when less than three orthogonal magnetic sources are used for localization. We preliminarily validate the proposed localization approach in simulation. Our method can achieve an average localization accuracy of 1 mm, 1° at an update rate of 150 Hz in a 0.2 x 0.2 x 0.2 m3 workspace below the actuator in the presence of sensor measurement noises and time delays, which shows its potential to be used for closed-loop control of the capsule. © 2022 IEEE.

关键词： Permanent magnets

Automatic Angle of Trunk Rotation Detection Using 3D Sensor Imaging in Scoliosis Assessment

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Automatic Angle of Trunk Rotation Detection Using 3D Sensor ...

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yang, Jie Zhao, Ziqi Xiao, Xiao Wang, Jiankun Meng, Max Q.-H. Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China The Yuanhua Robotics Perception & AI Technologies Ltd. Shenzhen518052 China Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Shenzhen Research Institute The Chinese University of HongKong Shenzhen China

ISBN: (纸本)9781665481090

Early detection of adolescent idiopathic scoliosis (AIS) is essential for AIS treatment and prevention of AIS progression. However, the existing clinical scoliosis assessment method, the standing full-column radiographs (X-ray) imaging, is radioactive, making this method unsuitable for large-scale promotion among adolescents. As a result, many countries have implemented school scoliosis screening programs (SSS) to achieve large-scale scoliosis screening and monitoring of adolescents by measuring the angle of trunk rotation (ATR). However, the SSS is time-consuming and inaccurate due to subjective manual examination. In this paper, we present an automatic method to calculate ATR based on the contour curve of the human back. This automatic method begins with a 3D depth sensor-scanned point cloud model of the human back and identifies the spinous process and stress points by obtaining the back contour curve from the depth information. Finally, the ATR is calculated according to the measurement principle of scoliosis meter. We demonstrate the effectiveness of our method using twenty-seven pairs of ATR data from nine participants with AFBT. There is not only a significant positive correlation, but also a convinced level of agreement between ATRs obtained using automatic method and ATRs obtained using manual method in the SSS. The experiment results reveal that the proposed method can efficiently achieve accurate measurement of ATR in the SSS. © 2022 IEEE.

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Overview Paper A Comprehensive Survey of Digital Image Steganography and Steganalysis

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APSIPA Transactions on Signal and Information Processing 2024年第1期13卷

作者： Luo, Weiqi Wei, Kangkang Li, Qiushi Ye, Miaoxin Tan, Shunquan Tang, Weixuan Huang, Jiwu School of Computer Science and Engineering Sun Yat-sen University China School of Software Nanchang University China Shenzhen Key Laboratory of Media Security Shenzhen University China Institute of Artificial Intelligence Guangzhou University China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University China

In the realm of digital communications, steganography and steganalysis have become a solution for securely exchanging covert information. This survey initiates with an exploration of the widely used passive-warden scenario model, analyzing its significance, key performance indicators, relevant databases, and clarifying some commonly misunderstood fundamental concepts associated with this model. Subsequently, the paper comprehensively examines the evolution and current state of digital image steganography and steganalysis, highlighting the transition from traditional handcrafted based methods to sophisticated deep learning based techniques developed over the past two decades. It offers thorough descriptions and evaluations of typical methods in both steganography and steganalysis, with a particular emphasis on deep learning-based techniques that have emerged in recent years. Furthermore, the survey identifies significant challenges currently faced in translating theoretical research into practical applications. By integrating these insights, the survey not only charts the historical development and technological advancements in steganography and steganalysis but also establishes a proactive agenda for future research aimed at enhancing security in covert communications. © 2024 W. Luo, K. Wei, Q. Li, M. Ye, S. Tan, W. Tang and J. Huang.

关键词： Steganalysis steganography

DYNAMIC LOW-RANK SPARSE ADAPTATION FOR LARGE LANGUAGE MODELS

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arXiv 2025年

作者： Huang, Weizhong Zhang, Yuxin Zheng, Xiawu Liu, Yang Lin, Jing Yao, Yiwu Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China Huawei Technologies China Institute of Artificial Intelligence Xiamen University China Peng Cheng Laboratory Shenzhen China

Despite the efficacy of network sparsity in alleviating the deployment strain of Large Language Models (LLMs), it endures significant performance degradation. Applying Low-Rank Adaptation (LoRA) to fine-tune the sparse LLMs offers an intuitive approach to counter this predicament, while it holds shortcomings include: 1) The inability to integrate LoRA weights into sparse LLMs post-training, and 2) Insufficient performance recovery at high sparsity ratios. In this paper, we introduce dynamic Low-rank Sparse Adaptation (LoSA), a novel method that seamlessly integrates low-rank adaptation into LLM sparsity within a unified framework, thereby enhancing the performance of sparse LLMs without increasing the inference latency. In particular, LoSA dynamically sparsifies the LoRA outcomes based on the corresponding sparse weights during fine-tuning, thus guaranteeing that the LoRA module can be integrated into the sparse LLMs post-training. Besides, LoSA leverages Representation Mutual Information (RMI) as an indicator to determine the importance of layers, thereby efficiently determining the layer-wise sparsity rates during fine-tuning. Predicated on this, LoSA adjusts the rank of the LoRA module based on the variability in layer-wise reconstruction errors, allocating an appropriate fine-tuning for each layer to reduce the output discrepancies between dense and sparse LLMs. Extensive experiments tell that LoSA can efficiently boost the efficacy of sparse LLMs within a few hours, without introducing any additional inferential burden. For example, LoSA reduced the perplexity of sparse LLaMA-2-7B by 68.73↓ and increased zero-shot accuracy by 16.32%↑, achieving a 2.60× speedup on CPU and 2.23× speedup on GPU, requiring only 45 minutes of fine-tuning on a single NVIDIA A100 80GB GPU. Code is available at https://***/wzhuang-xmu/LoSA. Copyright © 2025, The Authors. All rights reserved.

关键词： Graphics processing unit