Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Neuroethology, Computational

Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents

Assistive trajectories for human-in-the-loop mobile robotic platforms

Autonomous and semi-autonomous smoothly interruptible trajectories are developed which are highly suitable for application in tele-operated mobile robots, operator on-board military mobile ground platforms, and other mobility assistance platforms. These trajectories will allow a navigational system to provide assistance to the operator in the loop, for purpose built robots or remotely operated platforms. This will allow the platform to function well beyond the line-of-sight of the operator, enabling remote operation inside a building, surveillance, or advanced observations whilst keeping the operator in a safe location. In addition, on-board operators can be assisted to navigate without collision when distracted, or under-fire, or when physically disabled by injury

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems