Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

ULTRALIGHT RADAR FOR SMALL AND MICRO-UAV NAVIGATION

This paper presents a radar approach to navigation of small and micro Unmanned Aerial Vehicles (UAV) in environments challenging for common sensors. A technique based on radar odometry is briefly explained and schemes for complete integration with other sensors are proposed. The focus of the paper is set on ultralight radars and interpretation of outputs of such sensor when dealing with autonomous navigation in complex scenario. The experimental setup used to analyse the proposed approach comprises one multi-rotor UAV and one ultralight commercial radar. Results from flight tests in which both forward-only motion and mixed motion are presented and analysed, providing a reference for understanding outputs of radar in complex scenarios. The radar odometry solution is compared with ground truth provided by GPS sensor

ULTRALIGHT RADAR FOR SMALL AND MICRO-UAV NAVIGATION

This paper presents a radar approach to navigation of small and micro Unmanned Aerial Vehicles (UAV) in environments challenging for common sensors. A technique based on radar odometry is briefly explained and schemes for complete integration with other sensors are proposed. The focus of the paper is set on ultralight radars and interpretation of outputs of such sensor when dealing with autonomous navigation in complex scenario. The experimental setup used to analyse the proposed approach comprises one multi-rotor UAV and one ultralight commercial radar. Results from flight tests in which both forward-only motion and mixed motion are presented and analysed, providing a reference for understanding outputs of radar in complex scenarios. The radar odometry solution is compared with ground truth provided by GPS sensor

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit

Taking Inspiration from Flying Insects to Navigate inside Buildings

These days, flying insects are seen as genuinely agile micro air vehicles fitted with smart sensors and also parsimonious in their use of brain resources. They are able to visually navigate in unpredictable and GPS-denied environments. Understanding how such tiny animals work would help engineers to figure out different issues relating to drone miniaturization and navigation inside buildings. To turn a drone of ~1 kg into a robot, miniaturized conventional avionics can be employed; however, this results in a loss of their flight autonomy. On the other hand, to turn a drone of a mass between ~1 g (or less) and ~500 g into a robot requires an innovative approach taking inspiration from flying insects both with regard to their flapping wing propulsion system and their sensory system based mainly on motion vision in order to avoid obstacles in three dimensions or to navigate on the basis of visual cues. This chapter will provide a snapshot of the current state of the art in the field of bioinspired optic flow sensors and optic flow-based direct feedback loops applied to micro air vehicles flying inside buildings