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作者： Li, Rui Li, Yinlin Su, Xiaojie School of Automation Chongqing University Chongqing400040 China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Grasp planning and the grasping force optimization are challenging issues associated with the dexterous robotic hand grasping problem. To ensure a safe, fast, and stable grasp for robots, careful determination of the grasping points and the grasping forces is required. The former creates an open question because of the availability of multiple solutions. The latter is formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external forces. In this work, we propose a novel approach for the grasping force optimization of the 4-pin environmental constraint-based form-closure grasp using dynamic neural networks. To find a form-closure grasp, we propose an environmental constraint-based algorithm and convert it to a problem of finding the local minima of a constraint region, resulting in an easy determination of the grasping points with the guarantee of grasping safety. To satisfy the grasping force limit of the contact points, we solve the grasping force optimization problem with friction constraint, which maximize the efficiency of the applied force and power of the dexterous hand. The proposed approach is evaluated with 4-pin gripper, and the result demonstrates that we can plan a safe, fast, and stable grasp from any initial state to a form-closure grasping state. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Grippers

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Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance

Relative Localization for UAV-UGV Based on Correlation of Be...

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IEEE International Conference on Mechatronics and automation

作者： Jia Guo Kang Hu Jinting Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough.

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Speed control of a Biomimetic Robotic Fish Based on Linear Active Disturbance Rejection control

Speed Control of a Biomimetic Robotic Fish Based on Linear A...

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2021 China automation Congress, CAC 2021

作者： Huang, Yupei Wu, Zhengxing Du, Sheng The State Key Laboratory of Management and Control for Complex Systems Insititute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

This paper proposes a speed control method for a biomimetic robotic fish based on linear active disturbance rejection control. Inspired by a bluefin tuna in nature, a robotic fish with a two-joint propulsive mechanism and a crescent-shaped caudal fin is first designed. Thereafter, for a steady forward swimming speed, a speed control framework based on the linear form of active disturbance rejection control method is proposed, where the oscillation frequency of the caudal fin is adopted to regulate the swimming speed. Meanwhile, a swimming speed estimation approach based on the oscillation period is provided to effectively reduce the phenomenon of swimming speed fluctuation. Finally, both simulation and experiment results illustrate that the proposed method is effective for the speed control of robotic fish. © 2021 IEEE

关键词： Speed control

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Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road

Relative Roughness Measurement Based Real-Time Speed Plannin...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Liang Wang Tianwei Niu Shoukun Wang Shuai Wang Junzheng Wang Key Laboratory of Drive and Control of Servo Motion System School of Automation Beijing Institute of Technology Beijing China The State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China

In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On the one hand, the vertical profile of the lidar's point cloud data is converted from the time domain to the frequency domain in real time, and the integrated area of the sub-frequency range in the frequency domain is chosen as the relative roughness quantification value to realize the roughness quantification under various terrains. On the other hand, to model the relationship between vehicle speed and relative roughness, iterative search is utilized to create a speed and roughness model, and sliding windows are employed to update the roughness to achieve continuous mapping between speed and roughness. Ultimately, a number of tests were conducted on various rough roads using the oil exploration vehicle EV-56 as the study object. The experimental results show that the proposed method can identify the terrain roughness changes under complex terrain and change their speed within 0.2 m accuracy.

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DriveRP: RAG and Prompt Engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jun Huang Hao Ma Tengchao Zhang Fei Lin Siji Ma Xiao Wang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Artificial Intelligence Anhui University Hefei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models’ responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. This paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-Predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S" goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange Models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S" goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable.

关键词： Accuracy Trajectory planning Decision making Transportation Transforms Safety Digital twins Prompt engineering Motion control Autonomous vehicles

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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FIT: Frequency-based Image Translation for Domain Adaptive Object Detection

arXiv

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arXiv 2023年

作者： Zhang, Siqi Zhang, Lu Liu, Zhiyong Feng, Hangtao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Reinforcement Learning Applied to Agent Path Planning 41

Reinforcement Learning Applied to Agent Path Planning

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第41届中国控制会议

作者： Fenglan Sun Xiaoshuai Wu Guoxiong Long Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper provides an enhanced Q-learning algorithm for agent path planning in traditional grid maps,which solves the problem of agent path planning in grid *** an unknown environment,the classic Q-learning algorithm addresses the agent's path planning ***,there are several limits to this strategy in terms of path planning:the agent could only move nearby grids,and the step size is only one *** Q-learning algorithm,including changes the agent's direction and step *** action direction of agents is increased from four to *** movement step of agents is raised from one to three *** new method makes the convergence faster and proxy path ***,a set of simulation tests are presented to validate the modified Q-learning algorithm.

关键词： Agent Path planning Q-learning Grid method

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Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving

arXiv

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arXiv 2023年

作者： Li, Ding Zhang, Qichao Xia, Zhongpu Zhang, Kuan Yi, Menglong Jin, Wenda Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Baidu Inc. China

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents. Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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