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International Conference on Control, Automation and Systems ( ICCAS)

作者： Joon-Yeon Cho Jeong-Yean Yang Min-Seok Lee Dong-Soo Kwon Department of Mechanical Engineering KAIST Daejeon South Korea Department of Robotics Program KAIST Daejeon South Korea

Knee arthroplasty requires precise milling of the implant cavity, which can prolong implant life and thus, improve the surgical outcome. Robot knee arthroplasty can significantly increase precision of the milling for the implant cavity. However, robot systems often involve process of registration (matching coordinates of robot, patient, and the image data), which can be one of the major drawbacks. The purpose of this paper is to present a new registration method using a surface data extracted from a 3D laser scanner for registration in robotic arthroplasty. Registration for robotic arthroplasty requires the following steps: 3D reconstruction (3D model) of the distal femur using the bone counters extracted from computed tomography (CT) image sections; surface data extraction using a 3D laser scanner; and matching of the 3D model and the extracted surface data. Bone model mechanism was designed to estimate bone motion and available bone exposure. Surface data extracted using 3D laser scanner was matched with 3D model. ICP matching algorithm was employed to perform the spatial transformation between the 3D model and the surface data extracted using the 3D laser scanner. Four local regions (based on anatomical structure) of distal femur were tested to find RSR (Recommended Scanning Region). Issues regarding the application of 3D laser scanner to robotic knee arthroplasty (TKA) is presented and discussed.

关键词： Three dimensional displays Solid modeling Data models Laser modes Robots Surface emitting lasers Data mining

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Motion planning

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Computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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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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Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification

Traffic Signs Detection System Using YOLO (You Only Look Onc...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Yosua Setiawan Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road.

关键词： YOLO Vibrations Training Road accidents Accuracy Motors Liquid crystal displays Road safety Safety Videos

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Analyze and Predict Car Accidents Using Different Machine Learning Algorithms

Analyze and Predict Car Accidents Using Different Machine Le...

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International Conference on Information Management and Technology (ICIMTech)

作者： Farrell Putra Harimanto Chris Andrew Henry William Muhammad Zacky Asy'ari Samuel Albert Artanto Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

Traffic accidents can occur from various factors, ranging from the driver themselves to problems with the vehicle. Road and traffic accidents are the most significant factor of fatalities in the world. Most of the causes of accidents are the drivers themselves. Poor physical and mental conditions can significantly impact how to drive. The driving experience is also important; the longer the driver has been driving, the more professional he will be, and vice versa. Smart City solutions can predict the severity of accidents and give information to the authorities in order to reduce the number of fatalities. This paper investigates the factors that may lead to an accident on the road. By looking at data collected from traffic-related accidents and then visualizing it to make it easy to analyze; then, from this data, we will predict the causes of traffic accidents so they can be avoided. The best algorithm based on accuracy, MAE and RMSE is Random Forest Classifier with 0.8461, 0.1663 and 0.4376, respectively.

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Automation Lamp Healing Application based on Face Expression Identification System using Convolution Neural Network (CNN)

Automation Lamp Healing Application based on Face Expression...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Devin Onil Saduki Simangunsong Mahesh Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

The disruption of routines, education, recreation, as well as concerns about family income and health, has left many young people feeling fearful, angry, and worried about their future. In this system, a smart system will be developed that can capture, process, and interpret facial expressions so that a person's emotions can be easily and accurately identified, which is one of the best solutions. The system will use artificial intelligence that has been trained beforehand using suitable datasets. In the processed of developing the system, Face image with the size of 48 x 48 x 1 became the input of the system, processes by Convolution layer using Relu activation function. The activation function used in the output layer is softmax because it is suitable for more than 2 classifications. by applying CNN based on Adam optimization which have result on 98.96% and 70%, for training and testing accuracy, respectively. Furthermore, the system accurately turn-on the healing lamp based on the emotion identified.

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Topological Approximate Dynamic programming under Temporal Logic Constraints

arXiv

引用

arXiv 2019年

作者： Li, Lening Fu, Jie Robotics Engineering Program Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we develop a model-free approximate dynamic programming method for stochastic systems modeled as Markov decision processes to maximize the probability of satisfying high-level system specifications expressed in a subclass of temporal logic formulas—syntactically co-safe linear temporal logic. Our proposed method includes two steps: First, we decompose the planning problem into a sequence of sub-problems based on the topological property of the task automaton which is translated from a temporal logic formula. Second, we extend a model-free approximate dynamic programming method to solve value functions, one for each state in the task automaton, in an order reverse to the causal dependency. Particularly, we show that the run-time of the proposed algorithm does not grow exponentially with the size of specifications. The correctness and efficiency of the algorithm are demonstrated using a robotic motion planning example. Copyright © 2019, The Authors. All rights reserved.

关键词： Specifications

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Design of an Automatic Price Tag System for Web-Based Retail Business

Design of an Automatic Price Tag System for Web-Based Retail...

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International Conference on Information Management and Technology (ICIMTech)

作者： Suryadiputra Liawatimena Devina Gunawan Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper.

关键词： Electric potential Microcontrollers Pricing Manuals Information management Servers Wireless fidelity Faces Business

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Approximate dynamic programming with probabilistic temporal logic constraints

arXiv

引用

arXiv 2018年

作者： Li, Lening Fu, Jie Robotics Engineering Program Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we develop approximate dynamic programming methods for stochastic systems modeled as Markov Decision Processes, given both soft performance criteria and hard constraints in a class of probabilistic temporal logic called Probabilistic Computation Tree Logic (PCTL). Our approach consists of two steps: First, we show how to transform a class of PCTL formulas into chance constraints that can be enforced during planning in stochastic systems. Second, by integrating randomized optimization and entropy-regulated dynamic programming, we devise a novel trajectory sampling-based approximate value iteration method to iteratively solve for an upper bound on the value function while ensuring the constraints that PCTL specifications are satisfied. Particularly, we show that by the on-policy sampling of the trajectories, a tight bound can be achieved between the upper bound given by the approximation and the true value function. The correctness and efficiency of the method are demonstrated using robotic motion planning examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Car Engine and Chassis Assembly Training Simulation Using VR Technology

Car Engine and Chassis Assembly Training Simulation Using VR...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Miguel Yehezkiel Noya Muhammad Zacky Asy'ari Christian Bernard Kuswandi Zener Lie Sukra Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

The process of educating and learning about the automotive industry is still ongoing, and one of the most effective techniques is the provision of direct practice. Such a system is regarded as effective, albeit with certain downsides. One of them is the restricted area and time required to conduct the exercise, as well as the significant risk of accidents during vehicle engine assembly. Based on these justifications, Vehicle Engine Assembly Simulation with Virtual Reality (VR) Technology is now available as an alternate means of educating students about the automotive industry, particularly car engine assembly. This concept employs a virtual environment that allows users to perceive the automobile world in real time through an immersive experience. VR allows users to directly disassemble automobile engines. This virtual reality application was created utilizing the Oculus Quest 2 platform and the Unity Game Engine.

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