Swarm drone
221118637
Bidirectional Multi tier Cognitive Swarm Drone 5G Network
The use of unmanned aerial vehicle (UAV) as a dynamic node in point to point communication of 5G and beyond 5G cellular networks is an emerging technology. It provides a reliable and efficient mode of wireless communication network in the sky. Existing architectures include single tier and multi tier drone architecture. Major challenges in multi tier architecture is in establishing an air traffic control system and in efficient resource sharing between the drones. To overcome the challenges associated with the multitier drone and cellular drone networks, a bidirectional multitier cognitive swarm drone network (BMCSDN) is proposed and investigated in terms of spectrum efficiency and allocation of optimized altitude and power for down link transmission. The proposed method uses the cognitive swarm drone (CSD) concept and provides enhancement of capacity and coverage with optimized transmission efficiency. The work also analyses the effect of interference when all CSDs transmit and receive with static and dynamic frequency allocations.
IEEE INFOCOM 2020 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Sunil Jacob Varun G Menon Shynu Gopalan Padinjappurathu KS FathimaShemim Bandana Mahapatra Saira Joseph
235083475
The Aspects of Swarm Drone Terrorism and Its Effective Countermeasures
Hyunho Kim Park Hong-Sung Chae Kyu-Chil
231675831
RESEARCH ON SWARM DRONE USING WIRELESS NAVIGATION NETWORK IN PERFORMING BIRD MODEL SWARM DRONE
This research presented the preliminary research about swarm algorithm for simple maneuver of drones using wireless navigation network as a locator and communication. A simple and efficient solution to synchronize and orchestrate a swarm of drones lies in the RF communications to position drones where we can try a simple swarm algorithm. The communication control among drones used transmitter and receiver placed on each of the drones' wireless navigation network (WNN) The mechanism of drone motion can be directed into swarm motion where random motion is done by each drone without collation and leader which called as Apus Model Swarm Drone (AMSD) It can also be directed in the kind of following motion where the leader drone followed by other drones which completely autonomous which called Boid Model Swarm Drone (BMSD) The results obtained by placing the vertical obstacle randomly. The AMSD, although not perfectly in finished entire the swarm motion, given a promising result in the next ongoing research as a new novelty of experiment in swarm drone with adding extremely random motion in speed. Keyword: Swarm, drone, algorithm, wireless, navigation.
Yeffry Handoko
222219582
Deep Learning based Anomaly Detection for a Vehicle in Swarm Drone System
In this paper, we perform the actual verification of the anomaly detection (AD) model of each drone that indicates the anomaly in swarm drone flight using the actual flight data. For this purpose, we use a model based AD method that uses data accumulated through actual flight tests. The AD model uses a deep neural network based generation model to create a training model with normal data and perform tests with abnormal data. As a result, the diagnostic results of mainly three cases are derived and analyzed for validity. The proposed AD method can be integrated with a machine learning based framework that can immediately detect abnormal behavior of swarm drone flights, which can be utilized to improve the reliability of swarm drone flight operations.
2020 International Conference on Unmanned Aircraft Systems (ICUAS)
Hyojung Ahn
210848409
Learning Based Anomaly Detection and Monitoring for Swarm Drone Flights
This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator's naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one dimensional convolution layers followed by fully connected multi layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation.
Hyojung Ahn Han-Lim Choi Min-Soo Kang Sung Tae Moon
218517029
SwarmLab: a Matlab Drone Swarm Simulator
Among the available solutions for drone swarm simulations, we identified a lack of simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis. Moreover, users who want to dive in the research field of drone swarms often need to interface with multiple programming languages. We present SwarmLab, a software entirely written in MATLAB, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two decentralized algorithms from the state of the art for the navigation of aerial swarms in cluttered environments, Olfati Saber's and Vasarhelyi's. We analyze the variability of the inter agent distances and agents' speeds during flight. We also study some of the performance metrics presented, i.e. order, inter and extra agent safety, union, and connectivity. While Olfati Saber's approach results in a faster crossing of the obstacle field, Vasarhelyi's approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Enrica Soria Fabrizio Schiano Dario Floreano
211011238
Towards Brain Computer Interfaces for Drone Swarm Control
Noninvasive brain computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high level tasks.
2020 8th International Winter Conference on Brain Computer Interface (BCI)
Ji-Hoon Jeong Dae-Hyeok Lee Hyung-Ju Ahn Seong-Whan Lee
218628807
Swarm based Drone as a Service (SDaaS) for Delivery
We propose a novel framework for composing Swarm based Drone as a Service (SDaaS) for delivery. Two composition approaches, i.e. sequential and parallel are designed considering the different behaviors of drone swarms. The proposed framework considers various constraints, e.g. recharging time and limited battery to meet delivery deadlines. We propose SDaaS composition algorithms using a modified A* algorithm. A cooperative behavior model is incorporated to reduce recharging and waiting time in a delivery. Experimental results prove the efficiency of the proposed approach.
2020 IEEE International Conference on Web Services (ICWS)
Balsam Alkouz Athman Bouguettaya Sajib Mistry
216193444
Toward Robust and Intelligent Drone Swarm: Challenges and Future Directions
The rapid development of Space Air Ground integrated network, IoT, and swarm based robotic systems has promoted the transformation of traditional single drone toward drone swarm. Compared to the traditional single drone, drone swarm can collaboratively complete complex tasks with higher efficiency and lower cost, especially in harsh environments. Communication and networking techniques are essential to enabling collaborate information sharing, coordinating multiple drones, and achieving autonomous drone swarm. However, the traditional communication technologies on fixed networks or slowly moving networks cannot address the unique characteristics of drone swarm, such as high dynamic topology, intermittent links and capability constraints. Two kinds of networking techniques fit for different drone swarm tasks are investigated, and the performance indexes of several wireless technologies suitable for drone swarm are also analyzed. Considering that drone swarm would usually be deployed in dire circumstances and the network may get frequently partitioned, the robustness of drone swarm becomes crucial. Based on the Molloy Reed criterion, a swarm intelligent robust solution for drone swarm is proposed by using the consensus method and grey prediction, which has advantages of small overhead and local information exchanging. The simulation results corroborate that the robustness to node failure of drone swarm can be effectively improved by the proposed method.
IEEE Network
Wu Chen Jiajia Liu Hongzhi Guo Nei Kato
211001464
Distributed Deep Reinforcement Learning for Drone Swarm Control
We applied Deep Q Reinforcement Learning, with the use of CNNs, to manage a drone swarm scenario in a bi dimensional environment. Given a square map with some targets, the goal is to make the drones able to learn to cooperate between them, trying to track and follow the most valuable targets. We compared a distributed and a centralized approach and verified how the first can outperform the latter in a real world scenario with limited training.
Federico Venturini