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International Conference on Electrical Engineering and Informatics, ICEEI

作者： Samuel Raymond Pranowo Zener Lie Sukra Muhammad Zacky Asy'ari Marisa Paryasto Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electrical Engineering Department Computer Engineering Program Telkom University Bandung Indonesia

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%.

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： James Akl Shreedhar Kodate Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at ***/10.5281/zenodo.7734951.

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Damping Injection Learning for Robots with Actor-Critic Driven by Integral Sliding Manifolds

Damping Injection Learning for Robots with Actor-Critic Driv...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Alejandro Tevera-Ruiz Rodolfo Garcia-Rodriguez Vicente Parra-Vega Robotics and Advanced Manufacture-CINVESTAV Saltillo Mexico Postgraduate Program in Aerospace Engineering Univ. Politécnica Metropolitana de Hidalgo Mexico

Reinforcement Learning (RL) is characterized by enforcing a reward collection mechanism that qualifies task performance aimed at optimizing a value function that tunes the action (control). In contrast to control theory, RL satisfies two metrics simultaneously: the task metric commonly, defined as the task error, and the temporal difference (TD) error. Thus, the RL has been empowered to learn how to fulfill control tasks based on a learning stage that collects rewards that evaluate a second metric of the robot’s performance. In this way, including RL in control systems is an advantageous scheme for some critical tasks nowadays. Consequently, the stability can be guaranteed, largely missing in the RL’s literature, to safe system operation.. In this paper, we propose an Actor-Critic (AC) scheme using the Critic Neural Network (C-NN) to approximate a value function using the TD error reduction, whereas the adaptive tuning of damping injection gain is provided by the Actor NN (A-NN) to enforce stability in closed-loop finally; achieving asymptotic stability with an underlying integral sliding mode loop. In this way, our proposal contributes to a novel model-free RL scheme that yields local exponential convergence with a smooth control action. Numeric simulations for a 2-DoF robot manipulator are presented and discussed under different conditions..

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Developing an Integrated Cognitive Test (ICT) for Computerized Assessment of Cognitive Impairment Risk 15

Developing an Integrated Cognitive Test (ICT) for Computeriz...

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15th Biomedical Engineering International Conference, BMEiCON 2023

作者： Limpornchitwilai, Warissara Paengkumhag, Chatchai Jutharee, Wisanu Chamnongthai, Kosin Kaewkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics and Automation Bangkok Thailand King Mongkut's University of Technology Thonburi Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350345247

In an aging society, dementia is a notable issue that occurs as individuals grow older, impacting cognitive ability and emotional well-being. This has led to the development of cognitive assessment tools to facilitate early detection of cognitive impairment. Limited utilization of computerized tests across different countries is due to the mismatch of existing computerized tests between the educational and cultural backgrounds of the elderly;this study sought to address this issue by developing the Integrated Cognitive Test (ICT). ICT is designed as a computerized tool for assessing the risk of cognitive impairment in Thai older adults with an easy-to-use interface. Thirty-six elderly participants with diverse education levels and without prior cognitive impairment diagnoses participated in the study. They completed MoCA, MMSE, and ICT in a single-day experiment. The scores, reaction time, and overall completion time were recorded for analysis. The study found significant differences between ICT and MMSE scores (p © 2023 IEEE.

关键词： Decision trees

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Design and Implementation of Dynamic Motion Policy for Multi-Sensor Omnidirectional Mobile Arm Robot System in Human-Robot Coexisted Healthcare Workplace

Design and Implementation of Dynamic Motion Policy for Multi...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Yu-Hsiung Wu Tzuu-Hseng S. Li aiRobots Lab. MS Degree Program on AI Robotics National Cheng Kung University Tainan Taiwan R.O.C.

This paper presents the design and implementation of an Omnidirectional Mobile Arm Robot System (O-MARS) equipped with multiple sensors. The system integrates sensor information using Riemannian Motion Policies (RMP) to generate dynamic motion policies, which are continuously transmitted to the corresponding actuators of the robot. To facilitate dynamic motion policies for the robot arm and mobile robot, this paper incorporates Eye-in-Hand and Eye-to-Hand vision applications as well as self-collision avoidance. The integration of vision and LIDAR localization guides the mobile robot towards its goal position. Additionally, a virtual model of the robot’s environment is constructed, and a virtual-physical integration system is devised. Within this system, the decision-making process of the RMP involves establishing relevant connections between recognized modeled environmental obstacles and the robot’s digital twins. This capability enables the mobile robot to navigate around obstacles in a familiar environment. Furthermore, the inclusion of digital twins allows for the integration of LiDAR obstacle avoidance, empowering the robot to exhibit avoidance behavior when encountering unknown obstacles. The system also incorporates human skeletons and whole-body position information to autonomously analyze collision dynamics between humans and robots, ensuring their safe coexistence in shared environments. Lastly, a physical experiment is conducted in a healthcare workplace where humans and robots coexist, with a specific focus on the transportation of medical supplies. This experiment aims to demonstrate the system’s effectiveness in terms of accuracy, efficiency, and safety. This paper applies the RMP approach to various domains, including sensor integration, virtual-physical integration, and the heterogeneous application of manipulators and mobile robots. The experimental results of this paper can be found at https://***/watch?v=pRyd0-gvutM.

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RoboRover – A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following

RoboRover – A Jetson Nano Powered Robot Car for Object Foll...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trinh Huynh James Walter Federica Aveta Robotics Department University of Michigan Ann Arbor Michigan USA Electrical and Computer Engineering Program Wentworth Insitute of Technology Boston Massachusetts USA

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations.

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Robust reference tracking control subject to time-varying delay with future reference anticipation

Robust reference tracking control subject to time-varying de...

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IEEE Latin American robotics Symposium, LARS

作者： Mateus S. Meneses Bruno S. S. Pereira Tito L. M. Santos Graduate Program on Electric Eng. Federal University of Bahia (UFBA) Salvador Brazil Robotics dept. SENAI CIMATEC Salvador Brazil

This paper addresses the challenges of controlling systems over a network, considering the detrimental effects of network-induced delays and dynamic model uncertainties. To tackle these issues, a method is proposed that combines a stability criterion for systems with time-varying delays and model uncertainties with a predictive controller for linear systems, incorporating future references. The main objective is to ensure a given tolerance to network induced time-varying delays while accounting for model uncertainties and future references. The main motivation of the method is illustrated through simulations of an omnidirectional robot controlled remotely over a wireless network. The obtained results provide valuable insights into tuning the parameters of the unconstrained predictive controller in order to illustrate the usefulness of the proposed method in the presence of time-varying delays.

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Open-Source Autonomous Driving Software Platforms: Comparison of Autoware and Apollo

arXiv

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arXiv 2025年

作者： Jung, Hee-Yang Paek, Dong-Hee Kong, Seung-Hyun Robotics Program Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of CCS Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of

Full-stack autonomous driving system spans diverse technological domains—including perception, planning, and control—that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting infrastructure, from simulators and sensors to high-definition maps. These complexities with barrier to entry pose substantial limitations for individual developers and research groups. Recently, open-source autonomous driving software platforms have emerged to address this challenge by providing autonomous driving technologies and practical supporting infrastructure for implementing and evaluating autonomous driving functionalities. Among the prominent open-source platforms, Autoware and Apollo are frequently adopted in both academia and industry. While previous studies have assessed each platform independently, few have offered a quantitative and detailed head-to-head comparison of their capabilities. In this paper, we systematically examine the core modules of Autoware and Apollo and evaluate their middleware performance to highlight key differences. These insights serve as a practical reference for researchers and engineers, guiding them in selecting the most suitable platform for their specific development environments and advancing the field of full-stack autonomous driving system. Copyright © 2025, The Authors. All rights reserved.

关键词： Middleware

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Resilient Navigation Based on Multimodal Measurements and Degradation Identification for High-Speed Autonomous Race Cars

Resilient Navigation Based on Multimodal Measurements and De...

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IEEE Symposium on Intelligent Vehicle

作者： Daegyu Lee Hyunwoo Nam Chanhoe Ryu Sungwon Nah D. Hyunchul Shim Department of Electrical Engineering Korea Advanced Institute of Science and Technologies (KAIST) Daejeon Republic of Korea Robotics Program KAIST Daejeon Republic of Korea

This paper presents a localization system robust against unreliable measurements and a resilient navigation system recovering from localization failures for Indy autonomous challenge (IAC). The IAC is a competition with full-scale autonomous race cars that drive at speeds up to 300 kph. Owing to high-speed and heavy vibration in the car, a GPS/INS system is prone to degrade causing critical localization errors, which leads to catastrophic *** order to address this issue, we propose a robust localization system that probabilistically evaluates the credibility of multi-modal measurements. At a correction step of the Kalman filter, a degradation identification method with a novel hyper-parameter derived from Bayesian decision theory is introduced to choose the most credible measurement values in real-time. Since the racing condition is so harsh that even our robust localization method can fail for a short period of time, we present a resilient navigation system that enables the race car to continue to follow the race track in the event of a localization failure. Our system uses direct perception information in planning and execution until the completion of localization *** proposed localization system is first validated in a simulation with real measurement data contaminated by large artificial noises. The experimental validation during an actual race is also presented. The last part of our paper shows the results from the real-world tests where our system recovers from failures and prevents accidents in real-time, which proves the resilience of the proposed navigation system.

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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