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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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Coverage Path Planning of agricultural Spraying Drones with Graph Convolutional Network

Coverage Path Planning of Agricultural Spraying Drones with ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

关键词： Training Accuracy Graph convolutional networks Neural networks Spraying Traveling salesman problems Transformers Path planning Vehicle dynamics Genetic algorithms

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Physics-Informed Deep Learning-Based Monocular Vision and IMU Fusion for Extracting Position and Heading Data of Multirotors

Physics-Informed Deep Learning-Based Monocular Vision and IM...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Arshia Rezaei Erfan Radfar Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs.

关键词： Deep learning Accuracy Measurement units Magnetic sensors Motion estimation Estimation Autonomous aerial vehicles Robot sensing systems Robustness Real-time systems

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Comparing the Performance of Quaternion, Rotation Matrix, and Euler Angles Based Attitude PID controllers for Quadrotors

Comparing the Performance of Quaternion, Rotation Matrix, an...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Attitude states are critical parameters in the control, navigation, and guidance of aerial robotics. Various models, including Euler angles, rotation matrices, and quaternions, are used to represent the orientation of multirotors. The performance of a quadrotor's attitude controller is significantly influenced not only by optimal gain design but also by the structure and strategy of error allocation and control command management. Given the dynamic behavior of the quadrotor, challenges arise with the Proportional-Integral-Derivative (PID) controller, which, as a Single Input Single Output (SISO) controller, struggles to manage the system's channels independently. This paper presents three attitude control strategies for a quadrotor, focusing on both controller design and state estimation by utilizing three-dimensional spatial rotation models. The aim is to compare and improve quadrotor attitude control strategies by applying diverse criteria through these spatial rotation models. The models are implemented on a 3 Degrees of Freedom (DoF) quadrotor stand. The comparison evaluates tracking error, energy consumption, computational effort, and other key performance metrics.

关键词： Solid modeling Attitude control Quaternions Computational modeling 3-DOF Stability criteria Resource management State estimation Robots Quadrotors

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Dynamic Modeling and Sliding Mode control of an Over-Actuated Quadrotor with Variable Hedral Angle of Propeller Axes

Dynamic Modeling and Sliding Mode Control of an Over-Actuate...

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International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Ali Kafili Gavgani Amin Talaeizadeh Siamak Heidarzadeh Aria Alasty Hossein Nejat Pishkenari Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology Tehran Iran

The conventional quadrotor, characterized by four control inputs to control six degrees of freedom, is inherently under-actuated, resulting in limited independent control over pitch and roll angles. In this study, we present a configuration that incorporates four additional control inputs for manipulating the angles of the propeller axes. This addresses the previously mentioned limitations and offers additional advantages. Also, it can exhibit enhanced stability during altitude reduction by mitigating the vortex ring state. We derive the dynamic model for this system using Newthon-Euler's method. To address unmodeled dynamics, uncertainties, disturbances, and the inherent complexity of these systems, we utilize a nonlinear robust sliding mode controller. An affining process is performed to account for the non-affine nature of the dynamic equations. We introduce an optimization criteria aimed at maximizing battery life, leveraging the over-actuation of the system. This controller is applied to a dynamic system, infused with various uncertainties, such as inaccurate actuators, high-delayed servo motors, uncertain physical parameters, and wind disturbances. These factors collectively challenge the robustness of the control strategy. Additionally, the controller is implemented on a MATlab Simscape Multibody model. Given that the controller is model-based, the consistency of the obtained results validates the accuracy of the derived dynamic equations. This research aligns with the project SAC-1, one of the objectives of our lab, Sharif AgRolab.

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Self-Tuning PID controller for an Arm-Angle Adjustable Quadrotor Using an Actor-Critic-Based Neural Network

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadr...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

The increasing prevalence of multirotors necessitates the development of new configurations for specific applications. Innovative morphing of multirotors has enhanced their stability, maneuverability, agility, and overall performance. The utilization of Artificial Intelligence (AI) in robotics has enabled robots to exhibit autonomous behaviors in areas such as control, path planning, state estimation, system identification, and image processing. This paper introduces a novel approach to improving the control system of an adjustable arm-angle quadrotor through the integration of a self-tuning Proportional-Integral-Derivative (PID) controller, utilizing an Actor-Critic-based Neural Network (NN). Arm-angle adjustable quadrotors, with their complex and variable dynamics, present challenges in achieving optimal control performance across different arm angles. Traditional PID controllers often struggle to adapt efficiently to these dynamics. To address this issue, the Actor-Critic framework from reinforcement learning is employed to develop a self-tuning mechanism for the PID controller. The Actor network learns the optimal control actions, while the Critic network evaluates the performance of the control system, enabling continuous improvement. Extensive Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) tests demonstrate the effectiveness and feasibility of the proposed approach in achieving robust and adaptive control for tilt-arm quadrotors under diverse operating conditions. The results highlight the superiority of the Actor-Critic-based self-tuning PID controller over conventional methods, showcasing its potential for real-world devlopment in autonomous aerial systems.

关键词： Uncertainty Optimal control Reinforcement learning Artificial neural networks Autonomous aerial vehicles Real-time systems Adaptive control Robots Tuning Quadrotors

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A Practical Algorithm for Multirotor System Design and Evaluation

A Practical Algorithm for Multirotor System Design and Evalu...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Arshia Rezaei Faezeh Vahedi Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

Multirotors have attracted much attention in recent years. During the design phase, both designers and users are often curious about whether an assembled multirotor can meet their expectations, including hover endurance, system efficiency, maximum load capacity, maximum pitch angle, and maximum flight distance. Traditionally, multirotor performance evaluation involves multiple flight tests or relying on experience, which can be time-consuming and expensive. To solve this challenge, this algorithm is proposed for extensive offline design and evaluation. This algorithm primarily focuses on performance metrics linked to the propulsion system, encompassing components such as motors, propellers, electronic speed controllers, and batteries. In the initial stage of our research, we establish models for these propulsion system components. Notably, the input for these models comprises technical specifications provided by manufacturers. Using these models and their interrelationships, equations were formulated to describe different performance indicators. Then these equations were solved to perform performance evaluation. The effectiveness of this evaluation method is shown through an example developed by a powerful and user-friendly software that enables users to access the performance evaluation approach presented in this article.

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Combined Photoredox Catalysis for Value-Added Conversion of Contaminants at Spatially Separated Dual Active Sites

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research 2023年第3期6卷 597-607页

作者： Jieyuan Li Ruimin Chen Kaiwen Wang Yan Yang Jielin Wang Weiping Yang Shengyao Wang Guidong Yang Fan Dong Research Center for Carbon-Neutral Environmental and Energy Technology Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of ChinaChengdu 611731China Beijing Key Lab of Microstructure and Properties of Advanced Materials Beijing University of TechnologyBeijing 100124China School of Environmental Science and Engineering Institute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006GuangdongChina College of Science Huazhong Agricultural UniversityWuhan 430070China XJTU-Oxford Joint International Research Laboratory of Catalysis School of Chemical Engineering and TechnologyXi’an Jiaotong UniversityXi’an 710049China

As 2 indispensable counterparts in one catalysis system,the independent reduction and oxidation reactions require synergetic regulation for cooperatively promoting redox *** the current success in promoting the catalytic efficiency of half reduction or oxidation reactions,the lack of redox integration leads to low energy efficiency and unsatisfied catalytic ***,we exploit an emerging photoredox catalysis system by combining the reactions of nitrate reduction for ammonia synthesis and formaldehyde oxidation for formic acid production,in which superior photoredox efficiency is achieved on the spatially separated dual active sites of Ba single atoms and Ti^(3+).High catalytic redox rates are accomplished for respective ammonia synthesis(31.99±0.79 mmol gcat^(−1) h^(−1))and formic acid production(54.11±1.12 mmol gcat^(−1) h^(−1)),reaching a photoredox apparent quantum efficiency of 10.3%.Then,the critical roles of the spatially separated dual active sites are revealed,where Ba single atoms as the oxidation site using h+and Ti3+as the reduction site using e−are identified,*** efficient photoredox conversion of contaminants is accomplished with environmental importance and competitive economic *** study also represents a new opportunity to upgrade the conventional half photocatalysis into the complete paradigm for sustainable solar energy utilization.

关键词： redox catalysis accomplished

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 1-41页

作者： Huang, Shaoping Lou, Chuqian Zhou, Ying He, Zhao Jin, Xuejun Feng, Yuan Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) Shanghai Jiao Tong University Shanghai China Materials and Technology Center of Robotics Empa Dübendorf Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong University Shanghai China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Magnetic Resonance Imaging (MRI) is now a widely used modality for providing multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing radiation. In addition to its diagnostic potential, its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping, as well as integrated instrument localization, target recognition, and in situ, in vivo monitoring of the therapeutic efficacy. Areas of current applications include neurosurgery, breast biopsy, cardiovascular intervention, prostate biopsy and radiotherapy. Emerging applications in targeted drug delivery and MRI-guided chemoembolization are also being pursued. Whilst promising progress has been made in recent years, there are still significant basic science research and engineering challenges. This paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation, sensing, new materials, interventional instruments, and interactive/real-time MRI. Potential future research directions and new clinical developments are also discussed.

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Distribution of heavy metals in water and sediment of an urban river in a developing country:A probabilistic risk assessment

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International Journal of Sediment research 2022年第2期37卷 173-187页

作者： Mir Mohammad Ali Saima Rahman Md Saiful Islam Md Refat Jahan Rakib Shaharior Hossen Md Zillur Rahman Tapos Kormoker Abubakr M. Idris Khamphe Phoungthong Institute of Disaster Management Khulna University of Engineering and Technology Department of Aquaculture Sher-e-Bangla Agricultural University Department of Urban and Regional Planning Khulna University of Engineering and Technology Department of Soil Science Patuakhali Science and Technology University Department of Fisheries and Marine Science Noakhali Science and Technology University Department of Fisheries Science Chonnam National University Quality Control Chemistry Lab Department of Fisheries Department of Emergency Management Patuakhali Science and Technology University Department of Chemistry College of Science King Khalid University Research Center for Advanced Materials Science (RCAMS) King Khalid University Environmental Assessment and Technology for Hazardous Waste Management Research Center Faculty of Environmental Management Prince of Songkla University

River water and sediment embody environmental characteristics that give valuable eco-environmental *** to rapid industrialization,the aquatic environment of any urban river can be seriously polluted by heavy metals(HMs).The global concern is caused by heavy metal pollution because of its potential harm to aquatic ecosystems and human *** the Bhairab River,Bangladesh,surface sediment concentrations of globally alarming toxic metals such as arsenic(As),chromium(Cr),cadmium(Cd),and lead(Pb) were measured to determine the ecological and human health risks of the riverine *** average As,Cr,Cd,and Pb concentrations in water were 3.55,31.74,1.44,and23.82 μg/L,respectively,and in sediment were 4.13,34.17,1.66,and 25.46 mg/kg,*** the winter,metals in sediment were higher than during the *** most sediment samples,the enrichment factor(EF),contamination factor(CF),geoaccumulation index(I),pollution load index(PLI) of As,Cr,Cd,and Pb indicated moderate *** potential ecological risk(PER) in sediment followed the descending order of Cd> As> Pb> *** contamination level of toxic metals implied that the condition is frightening and probably severely affecting the aquatic ecology of this riverine ecosystem.

关键词： Toxic metals Environmental risk Bioaccumulation Ecological risk Bangladesh

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