Real-time autonomic decision making under uncertain environments for UAV-based search-and-rescue missions
Creators: name: Sadhu, Vidyasagar
Publisher: name: No Publisher Supplied
Year: 2020

description: Real-time smart and autonomous decision making in an Intelligent Physical System (IPS), e.g., an autonomous car or Unmanned Aerial Vehicle (UAV), involves two main stages---sensing (collection and then transformation of sensor data into actionable knowledge by giving semantic meaning to the raw data) and planning (making real-time decisions using this knowledge). The challenges faced by an IPS during these two stages include coping with the various forms of uncertainty caused by the (non-stationary) changing environment in which the IPS acts. These sources of uncertainty can be broadly classified into the following categories---data-quantity and -quality uncertainty, model and parameter uncertainty, environmental uncertainty, multi-agent non-stationarity, communication uncertainty, partial observability, and computing-resource uncertainty. Further, the degree of uncertainty in all these sources changes with time, introducing yet another challenge. In this dissertation, I propose novel solutions to deal with environmental, multi-agent non-stationarity, partial observability, and communication uncertainties, and present advanced techniques for real-time and autonomous operation of an IPS---or a group of IPSs---acting in a dynamic and unknown environment, primarily targeting UAV-based real-time autonomic search of objects/victims in a post-disaster scenario. To this end, I propose the following techniques needed to address the challenges mentioned above.1.Multi-IPS coordination---making high-level decisions in coordination with other UAVs acting in the environment in order to maximize the mission objective; this is challenging as the environment appears non-stationary from the view of any one UAV as other UAVs are taking actions independently. For this, I propose an actor-critic based Multi-Agent Deep Reinforcement Learning (MADRL) framework where the critic is trained in a centralized manner and the actor is decentralized and is used during deployment (testing).2.Imperfect communication and partial observability---the communication among the UAVs may not be perfect resulting in packet drops and delays; moreover, each UAV may not be able to observe the underlying state fully due to restricted field of view of the camera. For this, I propose to enhance the MADRL framework by augmenting it with Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) Neural Networks (NNs) to maintain state over time and to solve the partial observability/limited communication problem.3.Context-awareness and validation---context is a generic term used to denote the operating conditions e.g., environmental light/weather conditions, remaining operational time of a UAV, time, location, etc. It is well-known that context-aware decision-making yields better results than decision making done without taking context into account; this means that it is important to validate the context (obtained from sensors) especially in some secure missions. For this, I propose a Hidden Markov Model (HMM) based context modeling and prediction engine leveraging the history of personal and group behavior of the UAV; the proposed model can be used to predict the current (and also future) context of the UAV, which can be used to validate the sensor context.4.On-board proactive real-time monitoring and anomaly detection---as the UAVs are operating in a dynamic/uncertain environment, it is paramount to monitor proactively the operation of the UAV. For this, I propose a Convolutional Neural Network (CNN) and bi-directional LSTM (bi-LSTM) NN-based multi-task learning framework for real-time anomaly detection using UAV scalar sensor (non-image) data, e.g., accelerometer and gyroscope obtained from the on-board Inertial Measurement Unit (IMU).5.On-board diagnosis and anomaly identification---after detecting that an anomaly has happened, it is important to also classify/identify/diagnose the type of anomaly so as to determine which (sequence of) corrective actions need to be performed. For this, I propose a CNN-biLSTM based deep network classifier for classification of (pre-known) anomalies using IMU data; I also profile the performance of these techniques when run on drone-mountable/comparable hardware such as NVIDIA Jetson TX2 Graphical Processing Unit (GPU), Raspberry Pi B, etc.Finally, I thoroughly validate and assess the performance of all the techniques proposed in this dissertation towards enabling the application of real-time autonomous search missions using a group of UAVs operating under uncertain environments via computer simulations, hardware-in-the-loop emulations, and real-world experiments.