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Convolutional Neural Networks for Analyzing Unmanned Aerial Vehicles Sound
Shulin Li,HyunJong Kim,Sukhoon Lee,John C. Gallagher,Daeun Kim,SungWook Park,Eric T. Matson 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
The emergence of Unmanned Aerial Vehicles (UAV) is pervasive throughout society. A growing segment of usage is of a dubious nature for harassment, illegal activity and terrorism. Detection of unknown UAV’s has become a requirement for many organizations and agencies to thwart the emergence of UAV’s that are in some way threatening. To detect UAV, the use of acoustic signals has become an useful area of research. Convolutional Neural Networks (CNNs) are one of several models of deep learning, applied in various fields such as image recognition and natural language processing. In this project, we design a system to detect the presence of possible detection and payload detection using CNNs on the basis of sound data generated from UAV flights. The sound of recorded drones is pre-processed into spectral data by Fast Fourier Transform (FFT) and Mel-Frequency Cepstrum (MFCC) and given as the input value to the CNN model. The results show that it is possible to detect and differentiate UAVs which have standard weight and also with additional payload. In short, the project has two detection goals. One is the acoustic detection of a UAV, and the second is the determination if that UAV has a payload.
An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV
Kim, Yongho,Jung, Jin-Woo,Gallagher, John C.,Matson, Eric T. Korean Institute of Intelligent Systems 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2
In a dynamic environment autonomous robots often encounter unexpected situations that the robots have to deal with in order to continue proceeding their mission. We propose an adaptive goal-based model that allows cyber-physical systems (CPS) to update their environmental model and helps them analyze for attainment of their goals from current state using the updated environmental model and its capabilities. Information exchange approach utilizes Human-Agent-Robot-Machine-Sensor (HARMS) model to exchange messages between CPS. Model validation method uses NuSMV, which is one of Model Checking tools, to check whether the system can continue its mission toward the goal in the given environment. We explain a practical set up of the model in a situation in which homogeneous robots that has the same capability work in the same environment.
An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV
Yongho Kim,Jin-Woo Jung,John C. Gallagher,Eric T. Matson 한국지능시스템학회 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2
In a dynamic environment autonomous robots often encounter unexpected situations that the robots have to deal with in order to continue proceeding their mission. We propose an adaptive goal-based model that allows cyber-physical systems (CPS) to update their environmental model and helps them analyze for attainment of their goals from current state using the updated environmental model and its capabilities. Information exchange approach utilizes Human-Agent-Robot-Machine-Sensor (HARMS) model to exchange messages between CPS. Model validation method uses NuSMV, which is one of Model Checking tools, to check whether the system can continue its mission toward the goal in the given environment. We explain a practical set up of the model in a situation in which homogeneous robots that has the same capability work in the same environment.
A HARMS-based heterogeneous human-robot team for gathering and collecting
Kim, Miae,Koh, Inseok,Jeon, Hyewon,Choi, Jiyeong,Min, Byung Cheol,Matson, Eric T.,Gallagher, John Techno-Press 2018 Advances in robotics research Vol.2 No.3
Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.