Visibility in underwater robotics: Benchmarking and single image dehazing

Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image.Una de las dificultades más grandes de la robótica autónoma submarina es lidiar con la falta de visibilidad en imágenes submarinas. La transmisión de la luz en el agua degrada las imágenes dificultando el reconocimiento de objetos y en consecuencia la intervención. Ésta tesis se centra en el análisis del impacto de la degradación de las imágenes submarinas en algoritmos de visión a través de benchmarking, desarrollando un entorno de trabajo en la nube que permite analizar los resultados bajo diferentes condiciones. Teniendo en cuenta los resultados obtenidos con este entorno, se proponen métodos basados en técnicas de aprendizaje profundo para mitigar el impacto de la degradación de las imágenes en tiempo real introduciendo un paso previo que permita recuperar los colores originales

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Evolutionary control of autonomous underwater vehicles

The goal of Evolutionary Robotics (ER) is the development of automatic processes for the synthesis of robot control systems using evolutionary computation. The idea that it may be possible to synthesise robotic control systems using an automatic design process is appealing. However, ER is considerably more challenging and less automatic than its advocates would suggest. ER applies methods from the field of neuroevolution to evolve robot control systems. Neuroevolution is a machine learning algorithm that applies evolutionary computation to the design of Artificial Neural Networks (ANN). The aim of this thesis is to assay the practical characteristics of neuroevolution by performing bulk experiments on a set of Reinforcement Learning (RL) problems. This thesis was conducted with the view of applying neuroevolution to the design of neurocontrollers for small low-cost Autonomous Underwater Vehicles (AUV). A general approach to neuroevolution for RL problems is presented. The is selected to evolve ANN connection weights on the basis that it has shown competitive performance on continuous optimisation problems, is self-adaptive and can exploit dependencies between connection weights. Practical implementation issues are identified and discussed. A series of experiments are conducted on RL problems. These problems are representative of problems from the AUV domain, but manageable in terms of problem complexity and computational resources required. Results from these experiments are analysed to draw out practical characteristics of neuroevolution. Bulk experiments are conducted using the inverted pendulum problem. This popular control benchmark is inherently unstable, underactuated and non-linear: characteristics common to underwater vehicles. Two practical characteristics of neuroevolution are demonstrated: the importance of using randomly generated evaluation sets and the effect of evaluation noise on search performance. As part of these experiments, deficiencies in the benchmark are identified and modifications suggested. The problem of an underwater vehicle travelling to a goal in an obstacle free environment is studied. The vehicle is modelled as a Dubins car, which is a simplified model of the high-level kinematics of a torpedo class underwater vehicle. Two practical characteristics of neuroevolution are demonstrated: the importance of domain knowledge when formulating ANN inputs and how the fitness function defines the set of evolvable control policies. Paths generated by the evolved neurocontrollers are compared with known optimal solutions. A framework is presented to guide the practical application of neuroevolution to RL problems that covers a range of issues identified during the experiments conducted in this thesis. An assessment of neuroevolution concludes that it is far from automatic yet still has potential as a technique for solving reinforcement problems, although further research is required to better understand the process of evolutionary learning. The major contribution made by this thesis is a rigorous empirical study of the practical characteristics of neuroevolution as applied to RL problems. A critical, yet constructive, viewpoint is taken of neuroevolution. This viewpoint differs from much of the reseach undertaken in this field, which is often unjustifiably optimistic and tends to gloss over difficult practical issues

Shallow neural networks for autonomous robots

The use of Neural Networks (NNs) in modern applications is already well established thanks to the technological advancements in processing units and Deep Learning (DL), as well as the availability of deployment frameworks and services. However, the embedding of these methods in robotic systems is problematic when it comes to field operations. The use of Graphics Processing Units (GPUs) for such networks requires high amounts of power which would lead to shortened operational times. This is not desired since autonomous robots already need to manage their power supply to accommodate the lengths of their missions which can extend from hours to days. While external processing is possible, real-time monitoring can become unfeasible where delays are present. This also applies to autonomous robots that are deployed for underwater or space missions. For these reasons, there is a requirement for shallow but robust NN-based solutions that enhance the autonomy of a robot. This dissertation focuses on the design and meticulous parametrization complemented by methods that explain hyper-parameter importance. This is performed in the context of different settings and problems for autonomous robots in field operations. The contribution of this thesis comes in the form of autonomy augmentation for robots through shallow NNs that can potentially be embedded in future systems carrying NN processing units. This is done by implementing neural architectures that use sensor data to extract representations for event identification and learn patterns for event anticipation. This work harnesses Long Short-Term Memory networks (LSTMs) as the underpinning framework for time series representation and interpretation. This has been tested in three significant problems found in field operations: hardware malfunction classification, survey trajectory classification and hazardous event forecast and detection

Tractable robot simulation for terrain leveling

This thesis describes the problem of terrain leveling, in which one or more robots or vehicles are used to atten a terrain. The leveling operation is carried out either in preparation for construction, or for terrain reparation. In order to develop and prototype such a system, the use of simulation is advantageous. Such a simulation requires high fidelity to accurately model earth moving robots, which navigate uneven terrain and potentially manipulate the terrain itself. It has been found that existing tools for robot simulation typically do not adequately model deformable and/or uneven terrain. Software which does exist for this purpose, based on a traditional physics engine, is difficult if not impossible to run in real-time while achieving the desired accuracy. A number of possible approaches are proposed for a terrain leveling system using autonomous mobile robots. In order to test these approaches in simulation, a 2D simulator called Alexi has been developed, which uses the predictions of a neural network rather than physics simulation, to predict the motion of a vehicle and changes to a terrain. The neural network is trained using data captured from a high-fidelity non-real-time 3D simulator called Sandbox. Using a trained neural network to drive the 2D simulation provides considerable speed-up over the high-fidelity 3D simulation, allowing behaviour to be simulated in real-time while still capturing the physics of the agents and the environment. Two methods of simulating terrain in Sandbox are explored with results related to performance given for each. Two variants of Alexi are also explored, with results related to neural network training and generalization provided

EEG Classification with Discrete Wavelet Transforms and Energy Distribution

The overall aim of this investigation is to allow paralyzed individuals to regain motor movement with thought controlled robotic devices and in doing so provide them with a higher level of independence. The specific goal of this project is to classify EEG movement signals through a combination of discrete wavelet transforms and energy distribution. Using the energy distribution of these signals a neural network can be implemented to more accurately differentiate distinct motor movements