Insect inspired visual motion sensing and flying robots

International audienceFlying insects excellently master visual motion sensing techniques. They use dedicated motion processing circuits at a low energy and computational costs. Thanks to observations obtained on insect visual guidance, we developed visual motion sensors and bio-inspired autopilots dedicated to flying robots. Optic flow-based visuomotor control systems have been implemented on an increasingly large number of sighted autonomous robots. In this chapter, we present how we designed and constructed local motion sensors and how we implemented bio-inspired visual guidance scheme on-board several micro-aerial vehicles. An hyperacurate sensor in which retinal micro-scanning movements are performed via a small piezo-bender actuator was mounted onto a miniature aerial robot. The OSCAR II robot is able to track a moving target accurately by exploiting the microscan-ning movement imposed to its eye's retina. We also present two interdependent control schemes driving the eye in robot angular position and the robot's body angular position with respect to a visual target but without any knowledge of the robot's orientation in the global frame. This "steering-by-gazing" control strategy, which is implemented on this lightweight (100 g) miniature sighted aerial robot, demonstrates the effectiveness of this biomimetic visual/inertial heading control strategy

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

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

A multimodal micro air vehicle for autonomous flight in near-earth environments

Reconnaissance, surveillance, and search-and-rescue missions in near-Earth environments such as caves, forests, and urban areas pose many new challenges to command and control (C2) teams. Of great significance is how to acquire situational awareness when access to the scene is blocked by enemy fire, rubble, or other occlusions. Small bird-sized aerial robots are expendable and can fly over obstacles and through small openings to assist in the acquisition and distribution of intelligence. However, limited flying space and densely populated obstacle fields requires a vehicle that is capable of hovering, but also maneuverable. A secondary flight mode was incorporated into a fixed-wing aircraft to preserve its maneuverability while adding the capability of hovering. An inertial measurement sensor and onboard flight control system were interfaced and used to transition the hybrid prototype from cruise to hover flight and sustain a hover autonomously. Furthermore, the hovering flight mode can be used to maneuver the aircraft through small openings such as doorways. An ultrasonic and infrared sensor suite was designed to follow exterior building walls until an ingress route was detected. Reactive control was then used to traverse the doorway and gather reconnaissance. Entering a dangerous environment to gather intelligence autonomously will provide an invaluable resource to any C2 team. The holistic approach of platform development, sensor suite design, and control serves as the philosophy of this work.Ph.D., Mechanical Engineering -- Drexel University, 200

Flying Animal Inspired Behavior-Based Gap-Aiming Autonomous Flight with a Small Unmanned Rotorcraft in a Restricted Maneuverability Environment

This dissertation research shows a small unmanned rotorcraft system with onboard processing and a vision sensor can produce autonomous, collision-free flight in a restricted maneuverability environment with no a priori knowledge by using a gap-aiming behavior inspired by flying animals. Current approaches to autonomous flight with small unmanned aerial systems (SUAS) concentrate on detecting and explicitly avoiding obstacles. In contrast, biology indicates that birds, bats, and insects do the opposite; they react to open spaces, or gaps in the environment, with a gap_aiming behavior. Using flying animals as inspiration a behavior-based robotics approach is taken to implement and test their observed gap-aiming behavior in three dimensions. Because biological studies were unclear whether the flying animals were reacting to the largest gap perceived, the closest gap perceived, or all of the gaps three approaches for the perceptual schema were explored in simulation: detect_closest_gap, detect_largest_gap, and detect_all_gaps. The result of these simulations was used in a proof-of-concept implementation on a 3DRobotics Solo quadrotor platform in an environment designed to represent the navigational diffi- culties found inside a restricted maneuverability environment. The motor schema is implemented with an artificial potential field to produce the action of aiming to the center of the gap. Through two sets of field trials totaling fifteen flights conducted with a small unmanned quadrotor, the gap-aiming behavior observed in flying animals is shown to produce repeatable autonomous, collision-free flight in a restricted maneuverability environment. Additionally, using the distance from the starting location to perceived gaps, the horizontal and vertical distance traveled, and the distance from the center of the gap during traversal the implementation of the gap selection approach performs as intended, the three-dimensional movement produced by the motor schema and the accuracy of the motor schema are shown, respectively. This gap-aiming behavior provides the robotics community with the first known implementation of autonomous, collision-free flight on a small unmanned quadrotor without explicit obstacle detection and avoidance as seen with current implementations. Additionally, the testing environment described by quantitative metrics provides a benchmark for autonomous SUAS flight testing in confined environments. Finally, the success of the autonomous collision-free flight implementation on a small unmanned rotorcraft and field tested in a restricted maneuverability environment could have important societal impact in both the public and private sectors

Biologically Inspired Vision and Control for an Autonomous Flying Vehicle

This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods. ¶ ..

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr

Feasibility Study for an Autonomous UAV -Magnetometer System -- Final Report on SERDP SEED 1509:2206

Large areas across the United States are potentially contaminated with UXO, with some ranges encompassing tens to hundreds of thousands of acres. Technologies are needed which will allow for cost effective wide area scanning with 1) near 100 % coverage and 2) near 100 % detection of subsurface ordnance or features indicative of subsurface ordnance. The current approach to wide area scanning is a multi-level one, in which medium altitude fixed wing optical imaging is used for an initial site assessment. This assessment is followed with low altitude manned helicopter based magnetometry followed by surface investigations using either towed geophysical sensor arrays or man portable sensors. In order to be effective for small UXO detection, the sensing altitude for magnetic site investigations needs to be on the order of 1 – 3 meters. These altitude requirements means that manned helicopter surveys will generally only be feasible in large, open and relatively flat terrains. While such surveys are effective in mapping large areas relatively fast there are substantial mobilization/demobilization, staffing and equipment costs associated with these surveys (resulting in costs of approximately $100-$150/acre). Surface towed arrays provide high resolution maps but have other limitations, e.g. in their ability to navigate rough terrain effectively. Thus, other systems are needed allowing for effective data collection. An UAV (Unmanned Aerial Vehicle) magnetometer platform is an obvious alternative. The motivation behind such a system is that it would be safer for the operators, cheaper in initial and O&M costs, and more effective in terms of site characterization. However, while UAV data acquisition from fixed wing platforms for large (> 200 feet) stand off distances is relatively straight forward, a host of challenges exist for low stand-off distance (~ 6 feet) UAV geophysical data acquisition. The objective of SERDP SEED 1509:2006 was to identify the primary challenges associated with a low stand off distance autonomous UAV magnetometer platform and to investigate whether these challenges can be resolved successfully such that a successful UAV magnetometer platform can be constructed. The primary challenges which were identified and investigated include: 1. The feasibility of assembling a payload package which integrates magnetometers, accurate positioning systems (DGPS, height above ground measurement), obstacle avoidance systems, power infrastructure, communications and data storage as well as auxiliary flight controls 2. The availability of commercial UAV platforms with autonomous flight capability which can accommodate this payload package 3. The feasibility of integrating obstacle avoidance controls in UAV platform control 4. The feasibility of collecting high quality magnetic data in the vicinity of an UAV

Armstrong Research, Technology, and Engineering Accomplishments 2014

No abstract availabl