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Reactive Collision Avoidance control for Nonholonomic Vehicles and Obstacles of Arbitrary Shape

ASME Letters in Dynamic Systems and control 2024年第3期4卷 031005页

作者： Rodrıǵuez-Seda, Erick J. Department of Weapons Robotics and Control Engineering United States Naval Academy AnnapolisMD21402 United States

Potential field-based collision avoidance algorithms for mobile robots frequently assume vehicles and obstacles to have circular or spherical shapes. This assumption not only simplifies the analysis but also limits the mobility of agents in confined spaces, particularly for vehicles with elongated or irregular shapes. To increase mobility, this letter presents a decentralized collision avoidance framework for nonholonomic systems of unicycle type that considers the non-circular shape and relative orientation of vehicles and obstacles. The framework builds on the concepts of potential field and avoidance functions. However, it proposes using a non-constant minimum safe distance radius that changes based on the shape, relative position, and relative orientation of agents. The control framework is proven to guarantee collision avoidance at all times and is shown, via simulation, to increase the ability of agents to navigate through narrow spaces safely. © 2024 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Decentralized control

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Camera-LiDAR Fusion for Detecting Traffic Law Violations in Surveillance Systems

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Journal of Institute of control, robotics and Systems 2025年第3期31卷 187-195页

作者： Kim, Taek-Lim Jang, Byoung-Jin Yeon, Joo-Yeon Park, Tae Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Korea Republic of

Traffic surveillance systems are essential for ensuring public safety and optimizing urban traffic flow by accurately detecting, classifying, and monitoring traffic law violations such as illegal parking and jaywalking. However, detecting such violations in dynamic urban environments poses significant challenges due to unpredictable conditions. Traditional camera-based systems excel in capturing visual details but suffer performance degradation under low light, occlusion, or complex intersection scenarios, leading to false positives and missed detections. Therefore, we propose a camera-LiDAR fusion-based traffic violation detection system. LiDAR's independence from lighting conditions complements camera capabilities, especially in night scenarios or occluded environments. Experimental results in a real-world urban environment demonstrate the system’s effectiveness in reducing false positives and missed detections compared to traditional camera-only approaches. This study underscores the potential of sensor fusion to improve traffic management systems and points out future research opportunities in challenging conditions. © ICROS 2025.

关键词： Urban transportation

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Influence of Histogram Equalization on Multi-Classification of Retinal Diseases in OCT B-scans 27

Influence of Histogram Equalization on Multi-Classification ...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Poznan University of Technology Institue of Automatic Control and Robotics Department of Automatic Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9788362065486

The aim of this article is to analyze the impact of adaptive histogram equalization of the human eye OCT B-scans on the effectiveness of the classification of pathological changes. Tests were performed on two datasets. The first dataset contains 4 class B-scans (CNV, DME, DRUSEN, and NORMAL) taken with the use of Spectralis Heidelberg device, while the second dataset contains images from two devices (Spectralis Heidelberg OCT and Avanti Optovue OCT) and additionally contains the VMT (i.e., vitreomacular traction) class. The images were subjected to standard histogram equalization and contrast limited adaptive histogram equalization (CLAHE) with various parameters. The classification process was carried out using VGG16 artificial neural network. It can be observed that histogram equalization has no significant impact on improving classification performance. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Optical coherence tomography

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Image Segmentation System of Soft Capsules Based on Deep Learning

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Journal of Institute of control, robotics and Systems 2024年第6期30卷 607-613页

作者： Jung, Young-Gil Park, Tae-Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Pharmacists in conventional pharmacies and hospitals prepare and visually inspect medications before dispensing these medications to patients, thereby posing risks of exposure through skin contact or inhalation, as well as the potential for generating compounding errors such as incorrect medication types or quantities. To address the limitations of the manual compounding processes, automated compounding and medication-verification systems have been introduced and implemented. The automated compounding system prepares medications in pouch units based on prescriptions written by doctors, whereas the medication verification system verifies whether the types and quantities of the medication dispensed in these pouches match the prescription information. Medications are categorized broadly into solid tablets, hard capsules, and soft capsules. Color and shape information is used to classify medication types;solid tablets and hard capsules can be classified using rule-based image processing techniques such as image binarization. However, extracting shape information from soft capsules through which light passes is impossible via image binarization alone;therefore pattern matching is employed. Nevertheless, soft capsules change their forms, causing significant variations in pattern-matching similarity;therefore the detection accuracies of these capsules are low. To address this issue, this paper proposes a deep learning-based method for detecting soft capsules. © ICROS 2024.

关键词： Pattern matching

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Reinforcement Learning for Highway Driving Decision-making Using CDR (Curriculum based Domain Randomization)

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Journal of Institute of control, robotics and Systems 2025年第2期31卷 114-123页

作者： Yong, Jae-Hyoung Kim, Min-Sung Park, Tae-Hyoung Department of Intelligent System and Robotics Chungbuk National University Korea Republic of Department of Control Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Chungbuk National University Hospital Korea Republic of

Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision-making in highways is essential. However, decision-making in highways is a challenging task due to the complexity of traffic situations. Thus, reinforcement learning is an appropriate approach for decision-making in highways. However, a domain gap often arises between the training and testing environments due to differences in them. Therefore, a curriculum-based domain randomization approach is applied, which progressively adjusts the level of random parameters. The RL model is trained using the SUMO simulator and tested in the MORAI simulator, where white noise is added to simulate real-world conditions. Consequently, applying curriculum domain randomization has the lowest collision rate. © ICROS 2025.

关键词： Traffic congestion

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Preliminary Analysis of Physiological Signals in Relation to Early Indication of Paralysis Disorder Using Neural Network Approach

Preliminary Analysis of Physiological Signals in Relation to...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Kumar, Vijay Gupta, Sachin School of Electronics and Control Engieening Lovely Professional University Department of Robotics and Control Engineering Phagwara India

ISBN: (纸本)9798331540821

As per latest research in the main medical sector, paralysis sight as is sight as observed as one of the main foremost causes of disability in human subjects trailing only behind chronic illnesses such as cancer and heart disease. In this sight as study, we examined health-related signals to uncover potential indicators of paralysis related disability. In collaboration with medical experts, we identified eight crucial parameters that could assist in determining whether an individual shows symptom of paralysis. Our research involved assembling an observational database comprising over 40,000 human subjects, spanning the main period from January 2018 to December 2023. We employed a three-layer Recurrent Neural Network to conduct categorical analyses involving training, validation, and testing phases. Our results suggest that ECG, blood pressure, heart rate and PPG signals are essential in evaluating an individual's risk of paralysis sight as disorders. The main model achieved an impressive accuracy rate of 94.3%, outperforming other models observed during the main study. These observed findings underscore the main potential of advanced machine learning techniques for facilitating the main early detection of paralysis sight as symptoms. By providing timely notifications to healthcare providers and human subjects at risk, our methodology can significantly enhance patient care and outcomes. This sight as research not only enriches the main scientific understanding of paralysis sight as but also offers practical solutions for improving human health through timely intervention strategies. Ultimately aiding in the main prevention of severe consequences associated with this sight as debilitating condition. © 2024 IEEE.

关键词： Blood pressure

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A hierarchical framework for distributed resilient control of islanded AC microgrids under false data injection attacks

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ISA Transactions 2023年第1期139卷 191-204页

作者： Zarei, Mahdi Sadegh Atrianfar, Hajar Tehran Iran Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. © 2023 ISA

关键词： controllers

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Human-Centered Design and Development of an Elderly Care Robot 6

Human-Centered Design and Development of an Elderly Care Rob...

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Sajid, Ali Sultan, Muhammad Noor Ishfaq, Muhammad Muaviz Raza, Ali University of Engineering and Technology Lahore Human Centered Robotics Lab of NCRA Department of Mechatronics and Control Engineering Lahore Pakistan

ISBN: (纸本)9798350355703

Constituting to the demographic age shift in many regions of the world, technological advancement in the care sector, to deliver quality services to the elders, is now in more demand than ever. One such advancement is the evolution of socially assistive and care robots that promise sustainable, reliable, and safe human-robot interaction to meet geriatric needs. This paper presents the design and development of Silver Companion: a geriatric care robot for the elderly. The target audience chosen for the research phase is the socially isolated elderly community of East Asia and Central Europe which make up about 45% of their total elderly population. Considering the human-centered design paradigm, the technical and psychological aspects of the robot have been devised. The form and functions of the robot have been based on the product design principles. The whole development journey has been subjected to the identified elderly needs and the robot has been tested under multiple use-case scenarios, ideated for possibly fulfilling those needs. The robot has been equipped with assistive and interactive features like voice interaction, complementing micro-gestures and robot expressions, seamless navigation, and robot perception enhanced by a tilt-pan mechanism, all contributing towards making the robot capable of uplifting the well-being and quality of life of seniors. © 2024 IEEE.

关键词： Care Robots Geriatric Care Human Centered Design Human-Robot Interaction Micro Gestures Navigation Robot Expressions Robot Field of View Socially Assistive Robots Voice Interaction

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Coverage Path Planning of Agricultural Spraying Drones with Graph Convolutional Network 12

Coverage Path Planning of Agricultural Spraying Drones with ...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Khaloujini, Amirmasood Talaeizadeh, Amin Taheri, Alireza Alastya, Aria Advanced Research Lab for Control and Agricultural Robotics Sharif University of Technology Social and Cognitive Robotics Lab. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments. © 2024 IEEE.

关键词： Traveling salesman problem

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