Active Vision Strategies in Predation

Visual predation requires precise and accurate behaviour, for which many predators have evolved excellent visual skills. However, an animal's visual abilities are greatly affected by how it moves its eyes, known as active vision. Insects have immobile eyes but can direct their gaze by moving their heads and bodies. This thesis examines three predatory insects with different predatory strategies, to understand the extent to which active vision can be used in predation. The first experimental chapter considers the African praying mantid, Sphodromantis lineola. Praying mantids are stationary terrestrial predators, which use their extremely mobile necks to visually track prey until it is within reach. By using statistical models, we identified what factors elicited strikes and, importantly, their success rate. The timing of head movements greatly increased the chances of strike success, with earlier movements increasing the success rate. The second experimental chapter addresses how darting robber flies, Psilonyx annulatus, aerially attack static prey. Prior to attacking, darting robber flies translate their body around a central point, assessing their prey. After assessment, they attack from a position correlated with the target's absolute size, not its angular size. Prey is beyond the robber fly's stereopsis range during the period of assessment. Assessments of differently sized targets have similarities with the behaviour exhibited by jumping insects, which use motion parallax, a form of active vision, to assess jump distance, suggesting darting robber flies also use motion parallax to predate. The final experimental chapter considers killer flies, Coenosia attenuata, which chase moving targets aerially. Killer flies use a combination of gravity and wing acceleration to increase their speed when chasing prey from above. This increased speed restricts the flies' ability to steer. However, killer flies create strong looming stimuli which may trigger their prey to produce evasive manoeuvres, thereby slowing down. Moreover, by travelling faster towards their prey, killer flies may avoid losing track of it, a real danger when chasing moving prey with low- resolution eyes potentially avoided thanks to active vision. By employing active vision, each of the predators considered can achieve impressive performances, despite relying on very different strategies to capture prey. The use of active vision can increase the success of already excellent visual predators and improve the performance of predator with limited vision. However, active vision can also substantially alter predatory behaviour, leading to a trade- off between the advantages in visual perception active vision can bring and the disadvantage in behavioural efficiency of active vision strategies

Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems

Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.Comment: PhD thesis, Aerospace Engineering, Texas A&M (2020). For more information, see https://vggoecks.com

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews