369 research outputs found
Body swarm interface (BOSI) : controlling robotic swarms using human bio-signals
Traditionally robots are controlled using devices like joysticks, keyboards, mice and other
similar human computer interface (HCI) devices. Although this approach is effective and
practical for some cases, it is restrictive only to healthy individuals without disabilities,
and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI).
This work presents a novel concept of using human bio-signals to control swarms of
robots. With this concept there are two major advantages: Firstly, it gives amputees and
people with certain disabilities the ability to control robotic swarms, which has previously
not been possible. Secondly, it also gives the user a more intuitive interface to control
swarms of robots by using gestures, thoughts, and eye movement.
We measure different bio-signals from the human body including Electroencephalography
(EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf
products. After minimal signal processing, we then decode the intended control action
using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest
Neighbors (K-NN). We employ formation controllers based on distance and displacement
to control the shape and motion of the robotic swarm. Comparison for ground truth for
thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles
A Brief Exposition on Brain-Computer Interface
Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy
Securing a UAV Using Features from an EEG Signal
This thesis focuses on an approach which entails the extraction of Beta component of the EEG (Electroencephalogram) signal of a user and uses his/her EEG beta data to generate a random AES (Advanced Encryption Standard) encryption key. This Key is used to encrypt the communication between the UAVs (Unmanned aerial vehicles) and the ground control station. UAVs have attracted both commercial and military organizations in recent years. The progress in this field has reached significant popularity, and the research has incorporated different areas from the scientific domain. UAV communication became a significant concern when an attack on a Predator UAV occurred in 2009, which allowed the hijackers to get the live video stream. Since a UAVs major function depend on its onboard auto pilot, it is important to harden the system against vulnerabilities. In this thesis, we propose a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that gets activated in case the communication of the UAV from the ground control station gets attacked. This system was validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to an initially deployed "home" position
Cognitive Decay And Memory Recall During Long Duration Spaceflight
This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors
Human-Machine Interfaces for Service Robotics
L'abstract è presente nell'allegato / the abstract is in the attachmen
Low cost brain computer interface system for ar.drone control
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2017.Abstract : This work presents the design, implementation, and testing of a Brain-Computer Interface (BCI) system based on µ-waves to control the navigation of a drone. BCI systems perform the translation of brain signals into commands to communicate with external applications. The µ rhythm is a type of brain signal response to motor activity which can be easily measured by electroencephalography (EEG). For this reason, µ-waves based BCI systems have been extensively explored in the literature as a way of enabling patients with compromised neuromotor functions to interact with the outside world. To implement the signal processing and application interface routines, a software platform was built based on well-established filter and classification techniques, such as the Common Spatial Patterns (CSP) and the Linear Discriminant Analysis (LDA). For interfacing with the drone, an algorithm for translating the classifier outputs into drone commands was proposed. In addition, the acquisition of brain waves was performed by a low-cost and open-hardware EEG amplifier called OpenBCI. The validation of the designed system was performed using public and an acquired motor imagery EEG datasets, which were supplied to the platform to simulate the real-time performance of the system. The tests, conducted in a drone simulator, demonstrated the correct operation of the proposed methodology and the designed system.Este trabalho apresenta o projeto, implementação e teste de um sistema de Interface Cérebro Máquina (BCI) baseado em ondas µ para o controle da navegação de um drone comercial. Sistemas BCI realizam a tradução dos sinais cerebrais em comandos que podem ser usados para ativação e controle de aplicações externas. O ritmo µ é um tipo de resposta cerebral que é modulado através da atividade motora e pode ser facilmente medido através de eletroencefalografia (EEG). Por este motivo, sistemas BCI baseados em ondas µ tem sido extensivamente explorados na literatura como uma forma de permitir que pacientes com sistema neuromotor comprometido interajam com o ambiente externo. Neste trabalho, uma plataforma de software foi desenvolvida para implementar as rotinas de processamento de sinal e de interface com a aplicação. Ténicas bem estabelecidas como o filtro espacial CSP e o classificador LDA foram utilizadas para realizar a de-tecção dos padrões cerebrais. Além disso, é proposta uma metodologia para traduzir o sinal de saída do classificador em comandos que podem ser diretamente enviados para o drone. Para aquisição dos sinais de EEG, um amplificador de baixo custo e open-source chamado Open-BCI foi utilizado. A implementação do sistema foi validada através de um conjunto de dados público, que foram utilizados na plataforma como forma de simular o comportamento em tempo-real do sistema. Os testes de aplicação foram conduzidos em um simulador do drone, o que demonstrou o correto funcionamento da metodologia proposta e do sistema desenvolvido
Mental workload assessment for UAV traffic control using consumer-grade BCI equipment
The increasing popularity of unmanned aerial vehicles (UAVs) in critical applications makes supervisory systems based on the presence of human in the control loop of crucial importance. In UAV-traffic monitoring scenarios, where human operators are responsible for managing drones, systems flexibly supporting different levels of autonomy are needed to assist them when critical conditions occur. The assessment of UAV controllers' performance thus their mental workload may be used to discriminate the level and type of automation required. The aim of this paper is to build a mental-workload prediction model based on UAV operators' cognitive demand to support the design of an adjustable autonomy supervisory system. A classification and validation procedure was performed to both categorize the cognitive workload measured by ElectroEncephaloGram signals and evaluate the obtained patterns from the point of view of accuracy. Then, a user study was carried out to identify critical workload conditions by evaluating operators' performance in accomplishing the assigned tasks. Results obtained in this study provided precious indications for guiding next developments in the field
Recent Applications in Graph Theory
Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks
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