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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

Journal of Bionic Engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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Mobile Robot Autonomous Exploration Method with Improved Weighted Centroid and Multi-Objective Optimization 14

Mobile Robot Autonomous Exploration Method with Improved Wei...

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14th IEEE International Conference on Cyber technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Zhang, Yuze Shao, Shiliang Wang, Ting Cao, Fengkui Xu, Huiyang Shi, Xianyu Zhong, Ling School of Software Shenyang University of Technology Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798331506056

The frontier based exploration method is a traditional and direct method, which has the problems of redundancy of sensor observation range and simple frontier point selection rule. Based on the traditional frontier exploration, the process of identifying the frontier centroid points is improved, and the weight of each point is applied to represent the importance of different points to the exploration. Secondly, in view of the shortage of the traditional greedy strategy, the cost of avoiding obstacles and other factors are comprehensively considered, so as to adapt to a more complex environment. The experimental results show that the proposed method has high exploration efficiency and can complete the environment construction independently. © 2024 IEEE.

关键词： Mobile robots

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A new noise network and gradient parallelisation‐based asynchronous advantage actor‐critic algorithm

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IET Cyber-Systems and robotics 2022年第3期4卷 175-188页

作者： Zhengshun Fei Yanping Wang Jinglong Wang Kangling Liu Bingqiang Huang Ping Tan Provincial Key Institute of Robotics School of Automation and Electrical EngineeringZhejiang University of Science and TechnologyHangzhouChina State Key Lab of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Asynchronous advantage actor‐critic(A3C)algorithm is a commonly used policy opti-mization algorithm in reinforcement learning,in which asynchronous is parallel inter-active sampling and training,and advantage is a sampling multi‐step reward estimation method for computing *** order to address the problem of low efficiency and insufficient convergence caused by the traditional heuristic exploration of A3C algorithm in reinforcement learning,an improved A3C algorithm is proposed in this *** this algorithm,a noise network function,which updates the noise tensor in an explicit way is constructed to train the *** advantage estimation(GAE)is also adopted to describe the dominance ***,a new mean gradient parallelisation method is designed to update the parameters in both the primary and secondary networks by summing and averaging the gradients passed from all the sub‐processes to the main *** experiments were conducted in a gym environment using the PyTorch Agent Net(PTAN)advanced reinforcement learning library,and the results show that the method enables the agent to complete the learning training faster and its convergence during the training process is *** improved A3C algorithm has a better performance than the original algorithm,which can provide new ideas for sub-sequent research on reinforcement learning algorithms.

关键词： asynchronous advantage actorcritic (A3C) generalised advantage estimation (GAE) parallelisation reinforcement learning

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Contracting Forced Lagrangian and Contact Lagrangian Systems: application to nonholonomic systems with symmetries

Contracting Forced Lagrangian and Contact Lagrangian Systems...

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American Control Conference (ACC)

作者： Alexandre Anahory Simoes Leonardo Colombo School of Science and Technology IE University Spain Centre for Automation and Robotics (CSIC-UPM) Centre for Automation and Robotics (CSICUPM) Madrid Spain

In this paper we address the problem of identifying contracting systems among dynamical systems appearing in mechanics. First, we introduce a sufficient condition to identify contracting systems in a general Riemannian manifold. Then, we apply this technique to establish that a particular type of dissipative forced mechanical system is contracting, while stating immediate consequences of this fact for its stability. Finally, we use the previous results to study the stability of particular types of Contact and Nonholonomic systems.

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Application of Control Scheme for Trajectory Tracking of Hovercraft and Indoor Airship

Application of Control Scheme for Trajectory Tracking of Hov...

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International Conference on Methods and Models in automation and robotics (MMAR)

作者： Przemyslaw Herman Institute of Automation and Robotics Poznan University of Technology Poznan Poland

This paper is concerned with the application of a velocity transformation-based control scheme developed for underpowered marine vehicles to other vehicles with a similar mathematical model to the underwater vehicle. The problem of applicability of known control algorithms to systems that differ from the original is important in that its solution makes it possible to adapt a known control strategy for a similar class of vehicles. It is then not necessary to construct a new algorithm, but it is enough to transform one that is known. The effectiveness of the proposed approach was verified using simulations for a 3 DOF hovercraft model and an 3 DOF indoor airship.

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Speed and Distance Detection for Highway Traffic using Python

Speed and Distance Detection for Highway Traffic using Pytho...

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IEEE International Conference on System, Computation, automation and Networking (ICSCAN)

作者： G Renuka Devi S. Athiyan k. Ilan Thamizh V. Hema Varthini Department of Robotics and Automation Manakula Vinayagar Institute of Technology Puducherry

This Python -based scheme presents an innovative approach to bolstering road safety by harnessing the power of computer vision and machine learning. The primary objective is to provide drivers with crucial real-time information for informed lane change decisions, thereby reducing the risk of accidents. The system integrates advanced techniques such as object detection, tracking algorithms, and depth estimation to analyze video feeds from highway cameras. The system's core functionality involves estimating the speed and distance of vehicles on the highway. Object detection algorithms, including YOLO, SSD, and Faster R-CNN, are employed to identify and track vehicles in the video feed. Depth estimation techniques enhance accuracy by determining relative distances between vehicles. Machine learning algorithms, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), predict future positions and speeds, enabling the system to identify potential conflicts.

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Trash Detection in Water Bodies Using Yolo With Explainable AI Insight

Trash Detection in Water Bodies Using Yolo With Explainable ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Shafaq Rasheed Amal Mirza Muhammad Saad Saeed Muhammad Haroon Yousaf Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation Taxila Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models.

关键词： Deep learning YOLO Visualization Explainable AI Storms Biological system modeling Soil Water pollution Waste disposal robotics and automation

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Design Considerations of Outer-Rotor Flux-Modulated Permanent-Magnet In-Wheel Motors 32

Design Considerations of Outer-Rotor Flux-Modulated Permanen...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Huang, Zekang Luo, Yixiao Mi, Wenyuan Cai, Zheng Zhao, Fei Zhao, Hang Yu, Jincheng School of Mechanical Engineering and Automation Shenzhen China Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China Robotics and Autonomous Systems Thrust Guangzhou China

ISBN: (纸本)9798350399714

This paper presents the design considerations of outer-rotor flux-modulated permanent-magnet (OR-FMPM) in-wheel motors applied to light electric vehicles (EVs). The basic wheel model and drive system specifications are considered in the motor design, with utility of the flat-wire armature windings. The multi-step motor optimization focusing on the outer-rotor PM machines is carried out to reach the design objectives of the drive system, with auxiliary of the sensitivity analysis to lower the computing burden. Finally, the motor electromagnetic performances are specifically analyzed, verifying the feasibility of the motor design in the application of in-wheel motors. © 2023 IEEE.

关键词： Multiobjective optimization

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Design and Analysis of an Adaptive Robotic Gripper 1

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International Conference on Production and Industrial Engineering, CPIE 2023

作者： Patil, Yashraj M. Jamadar, N.I. Patil, Lalit N. Bhosale, Digvijay G. Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Automation and Robotics Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India

ISBN: (数字)9789819974450

ISBN: (纸本)9789819974443

In this study, an inexpensive fundamental robotic end effector module was investigated in order to make it easier to conduct robotic grasping research. The three-finger under actuated robotic gripper was designed in three dimensions and produced using a servomotor actuator that is readily available off the market and a small number of 3D printed parts. Following a comprehensive explanation of an under actuated finger, gear train mechanisms, in addition to general gripper assembly design, an illustration and explanation of the grabbing of objects with varied geometries follow. For research and instructional reasons, robotic researchers can utilize the offered open-source gripper design as a base to create custom robotic end effectors. An innovative prototype and a thorough introduction as well as an explanation of the gripper design idea are given. Using a four-bar linkage mechanical system and a single off-the-shelf actuator, it is demonstrated that the proposed robotic gripper with a single actuator satisfies the proposed objectives for its simple mechanical structure, low cost, and relatively high payload compared to similarly sized tendon-driven robotic end effectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mechanical actuators

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Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery 12th

Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery

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12th International Conference on Image and Graphics, ICIG 2023

作者： Cheng, Pengchao Tang, Xu Liang, Wenqi Li, Yu Cong, Wei Zang, Chuanzhi School of Artificial Intelligence Shenyang University of Technology Shenyang10142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031463105

With the rapid development of drones, tiny object detection in drone-captured scenarios has become a challenge task. However, the altitude of the drone changes while flying lead to the scale of the object changes dramatically. In addition, drones flying quickly cause motion blur on the densely tiny objects. In order to address the two issues mention above, we propose Tiny-YOLOv7. In order to detect multi-scale objects, we replace the original prediction heads with transformer prediction heads. For the motion blur issue, we propose DBS module to extract more visual elements and maintain computational cost of model. The DBS module consists of Dynamic Region-Aware Convolution (DRConv), Batch Normalization and Silu modules. On scenarios with dense objects, we additionally incorporate the Convolutional Block Attention Model (CBAM) to find the attention region of dense objects. Tiny-YOLOv7 is an effective and elegant method for handing tiny object detection. We validate our model through extensive experiments on VisDrone2021 and DOTA-v1.0 datasets. The results show that our method obtains remarkable improvements over the other models. In VisDrone2021 dataset, the mAP result of our method is 39.22 %, which is higher than SOTA method by 1.07 %. Furthermore, experiments on dataset DOTA-v1.0 demonstrate generalization of the propose model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Object detection

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