1,230 research outputs found
Artificial Intelligence based multi-agent control system
Le metodologie di Intelligenza Artificiale (AI) si occupano della possibilità di rendere le macchine in grado di compiere azioni intelligenti con lo scopo di aiutare l’essere umano; quindi è possibile affermare che l’Intelligenza Artificiale consente di portare all’interno delle macchine, caratteristiche tipiche considerate come caratteristiche umane.
Nello spazio dell’Intelligenza Artificiale ci sono molti compiti che potrebbero essere richiesti alla macchina come la percezione dell’ambiente, la percezione visiva, decisioni complesse.
La recente evoluzione in questo campo ha prodotto notevoli scoperte, princi- palmente in sistemi ingegneristici come sistemi multi-agente, sistemi in rete, impianti, sistemi veicolari, sistemi sanitari; infatti una parte dei suddetti sistemi di ingegneria è presente in questa tesi di dottorato.
Lo scopo principale di questo lavoro è presentare le mie recenti attività di ricerca nel campo di sistemi complessi che portano le metodologie di intelligenza artifi- ciale ad essere applicati in diversi ambienti, come nelle reti di telecomunicazione, nei sistemi di trasporto e nei sistemi sanitari per la Medicina Personalizzata. Gli approcci progettati e sviluppati nel campo delle reti di telecomunicazione sono presentati nel Capitolo 2, dove un algoritmo di Multi Agent Reinforcement Learning è stato progettato per implementare un approccio model-free al fine di controllare e aumentare il livello di soddisfazione degli utenti; le attività di ricerca nel campo dei sistemi di trasporto sono presentate alla fine del capitolo 2 e nel capitolo 3, in cui i due approcci riguardanti un algoritmo di Reinforcement Learning e un algoritmo di Deep Learning sono stati progettati e sviluppati per far fronte a soluzioni di viaggio personalizzate e all’identificazione automatica dei mezzi trasporto; le ricerche svolte nel campo della Medicina Personalizzata sono state presentate nel Capitolo 4 dove è stato presentato un approccio basato sul controllo Deep Learning e Model Predictive Control per affrontare il problema del controllo dei fattori biologici nei pazienti diabetici.Artificial Intelligence (AI) is a science that deals with the problem of having machines perform intelligent, complex, actions with the aim of helping the human being. It is then possible to assert that Artificial Intelligence permits to bring into machines, typical characteristics and abilities that were once limited to human intervention. In the field of AI there are several tasks that ideally could be delegated to machines, such as environment aware perception, visual perception and complex decisions in the various field.
The recent research trends in this field have produced remarkable upgrades mainly on complex engineering systems such as multi-agent systems, networked systems, manufacturing, vehicular and transportation systems, health care; in fact, a portion of the mentioned engineering system is discussed in this PhD thesis, as most of them are typical field of application for traditional control systems.
The main purpose if this work is to present my recent research activities in the field of complex systems, bringing artificial intelligent methodologies in different environments such as in telecommunication networks, transportation systems and health care for Personalized Medicine.
The designed and developed approaches in the field of telecommunication net- works is presented in Chapter 2, where a multi-agent reinforcement learning algorithm was designed to implement a model-free control approach in order to regulate and improve the level of satisfaction of the users, while the research activities in the field of transportation systems are presented at the end of Chapter 2 and in Chapter 3, where two approaches regarding a Reinforcement Learning algorithm and a Deep Learning algorithm were designed and developed to cope with tailored travels and automatic identification of transportation moralities. Finally, the research activities performed in the field of Personalized Medicine have been presented in Chapter 4 where a Deep Learning and Model Predictive control based approach are presented to address the problem of controlling biological factors in diabetic patients
Isesõitvate autode tee planeerimine baseerudes inimese tegevuse tuvastamisele
Human activity recognition (HAR) is wide research topic in a field of computer science. Improving
HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine
and in the field of autonomous vehicles. The system that is able to recognise human and its
activity without any errors and anomalies, would lead to safer and more empathetic autonomous
systems. During this thesis multiple neural networks models, with different complexity,
are being investigated. Each model is re-trained on the proposed unique data set, gathered on
automated guided vehicle (AGV) with the latest and the modest sensors used commonly on
autonomous vehicles. The best model is picked out based on the final accuracy for action recognition.
Best models pipeline is fused with YOLOv3, to enhance the human detection. In
addition to pipeline improvement, multiple action direction estimation methods are proposed.
The action estimation of the human is very important aspect for self-driving car collision free
path planning
Data Fusion Methods and Algorithms in the Context of Autonomous Systems - A path planning algorithms analysis and optimization exploiting fused data
L'abstract è presente nell'allegato / the abstract is in the attachmen
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV
This work explores the feasibility of steering a drone with a (recurrent)
neural network, based on input from a forward looking camera, in the context of
a high-level navigation task. We set up a generic framework for training a
network to perform navigation tasks based on imitation learning. It can be
applied to both aerial and land vehicles. As a proof of concept we apply it to
a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a
room containing a number of obstacles. So far only feedforward neural networks
(FNNs) have been used to train UAV control. To cope with more complex tasks, we
propose the use of recurrent neural networks (RNN) instead and successfully
train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision
based control is a sequential prediction problem, known for its highly
correlated input data. The correlation makes training a network hard,
especially an RNN. To overcome this issue, we investigate an alternative
sampling method during training, namely window-wise truncated backpropagation
through time (WW-TBPTT). Further, end-to-end training requires a lot of data
which often is not available. Therefore, we compare the performance of
retraining only the Fully Connected (FC) and LSTM control layers with networks
which are trained end-to-end. Performing the relatively simple task of crossing
a room already reveals important guidelines and good practices for training
neural control networks. Different visualizations help to explain the behavior
learned.Comment: 12 pages, 30 figure
RAP: Risk-Aware Prediction for Robust Planning
Robust planning in interactive scenarios requires predicting the uncertain
future to make risk-aware decisions. Unfortunately, due to long-tail
safety-critical events, the risk is often under-estimated by finite-sampling
approximations of probabilistic motion forecasts. This can lead to
overconfident and unsafe robot behavior, even with robust planners. Instead of
assuming full prediction coverage that robust planners require, we propose to
make prediction itself risk-aware. We introduce a new prediction objective to
learn a risk-biased distribution over trajectories, so that risk evaluation
simplifies to an expected cost estimation under this biased distribution. This
reduces the sample complexity of the risk estimation during online planning,
which is needed for safe real-time performance. Evaluation results in a
didactic simulation environment and on a real-world dataset demonstrate the
effectiveness of our approach.Comment: 23 pages, 14 figures, 3 tables. First two authors contributed
equally. Conference on Robot Learning (CoRL) 2022 (oral
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