Research issues in biological inspired sensors for flying robots

Biological inspired robotics is an area experiencing an increasing research and development. In spite of all the recent engineering advances, robots still lack capabilities with respect to agility, adaptability, intelligent sensing, fault-tolerance, stealth, and utilization of in-situ resources for power when compared to biological organisms. The general premise of bio-inspired engineering is to distill the principles incorporated in successful, nature-tested mechanisms of selected features and functional behaviors that can be captured through biomechatronic designs and minimalist operation principles from nature success strategies. Based on these concepts, robotics researchers are interested in gaining an understanding of the sensory aspects that would be required to mimic nature design with engineering solutions. In this paper are analysed developments in this area and the research aspects that have to be further studied are discussed.N/

biomimetic sensor suite for flight control of a micromechanical flying insect: design and experimental results, in

Four types of biomimetic sensors have been designed and simulated for flight control of a robotic flying insect. The ocelli use four photodiodes to detect changes in light intensity in the surrounding. The halteres use piezo-actuated vibrating structures to sense the Coriolis forces to detect angular velocities. The optic flow sensors consist of linear arrays of elementary motion detectors to register optic flows. The MEMS compass uses three metal loops to detect changes in the magnetic field. Despite simplicity and novelty, the preliminary tests on these devices showed promising performances for using such biomimetic sensors on a robotic flying insect.

Robust Control of Flapping-Wing in Micro Aerial Vehicle to have a Smooth Flapping Motion

This paper in first sections, will give a brief overview of both the purpose and the challenges facing the actuator and structure of Micromechanical Flying Insects (MFIs) and, in the last sections, an appropriate controller will developed for flapping motion. A hierarchical architecture that divides the control unit into three main levels is introduced. This approach break a complex control problem into a multi-level set of smaller control schemes, each of which is responsible for a clearly defined task. Also, the controller at each level can be designed independently of those in other levels. A fourbar mechanism for the wing displacement amplification, and a new system for fourbar mechanism actuation (wing actuation) is developed. We will develop a flexible beam with piezoelectric actuators and sensor (called Smart Beam) that will used to excite the fourbar mechanism for flapping mode of flight. The Frequency Response Function (FRF) of the smart beam was obtained from a Finite Element (FE) model and experimental system identification. The corresponding transfer function was derived from the mu synthesis and several robust controllers were then designed to control the beam to reach a smooth flapping motion. Besides excitation of the fourbar mechanism, the Smart beam will be used to control of noise and disturbance in the structure of the wing system

FREQUENCY DOMAIN CHARACTERIZATION OF OPTIC FLOW AND VISION-BASED OCELLAR SENSING FOR ROTATIONAL MOTION

The structure of an animal’s eye is determined by the tasks it must perform. While vertebrates rely on their two eyes for all visual functions, insects have evolved a wide range of specialized visual organs to support behaviors such as prey capture, predator evasion, mate pursuit, flight stabilization, and navigation. Compound eyes and ocelli constitute the vision forming and sensing mechanisms of some flying insects. They provide signals useful for flight stabilization and navigation. In contrast to the well-studied compound eye, the ocelli, seen as the second visual system, sense fast luminance changes and allows for fast visual processing. Using a luminance-based sensor that mimics the insect ocelli and a camera-based motion detection system, a frequency-domain characterization of an ocellar sensor and optic flow (due to rotational motion) are analyzed. Inspired by the insect neurons that make use of signals from both vision sensing mechanisms, advantages, disadvantages and complementary properties of ocellar and optic flow estimates are discussed

BIO-INSPIRED DISTURBANCE REJECTION WITH OCELLAR AND DISTRIBUTED ACCELERATION SENSING FOR SMALL UNMANNED AIRCRAFT SYSTEMS

Rapid sensing of body motions is critical to stabilizing a flight vehicle in the presence of exogenous disturbances as well as providing high performance tracking of desired control commands. This bandwidth requirement becomes more stringent as vehicle scale decreases. In many flying insects three simple eyes, known as the ocelli, operate as low latency visual egomotion sensors. Furthermore many flying insects employ distributed networks of acceleration-sensitive sensors to provide information about body egomotion to rapidly detect external forces and torques. In this work, simulation modeling of the ocelli visual system common to flying insects was performed based on physiological and behavioral data. Linear state estimation matrices were derived from the measurement models to form estimates of egomotion states. A fully analog ocellar sensor was designed and constructed based on these models, producing state estimation outputs. These analog state estimate outputs were characterized in the presence of egomotion stimuli. Feedback from the ocellar sensor, with and without complementary input from optic flow sensors, was implemented on a quadrotor to perform stabilization and disturbance rejection. The performance of the closed loop sensor feedback was compared to baseline inertial feedback. A distributed array of digital accelerometers was constructed to sense rapid force and torque measurements. The response of the array to induced motion stimuli was characterized and an automated calibration algorithm was formulated to estimate sensor position and orientation. A linear state estimation matrix was derived from the calibration to directly estimate forces and torques. The force and torque estimates provided by the sensor network were used to augment the quadrotor inner loop controller to improve tracking of desired commands in the presence of exogenous force and torque disturbances with a force-adaptive feedback control

Aircraft Attitude Estimation Using Panoramic Images

This thesis investigates the problem of reliably estimating attitude from panoramic imagery in cluttered environments. Accurate attitude is an essential input to the stabilisation systems of autonomous aerial vehicles. A new camera system which combines a CCD camera, UltraViolet (UV) filters and a panoramic mirror-lens is designed. Drawing on biological inspiration from the Ocelli organ possessed by certain insects, UV filtered images are used to enhance the contrast between the sky and ground and mitigate the effect of the sun. A novel method for real–time horizon-based attitude estimation using panoramic image that is capable of estimating an aircraft pitch and roll at a low altitude in the presence of sun, clouds and occluding features such as tree, building, is developed. Also, a new method for panoramic sky/ground thresholding, consisting of a horizon– and a sun–tracking system which works effectively even when the horizon line is difficult to detect by normal thresholding methods due to flares and other effects from the presence of the sun in the image, is proposed. An algorithm for estimating the attitude from three–dimensional mapping of the horizon projected onto a 3D plane is developed. The use of optic flow to determine pitch and roll rates is investigated using the panoramic image and image interpolation algorithm (I2A). Two methods which employ sensor fusion techniques, Extended Kalman Filter (EKF) and Artificial Neural Networks (ANNs), are used to fuse unfiltered measurements from inertial sensors and the vision system. The EKF estimates gyroscope biases and also the attitude. The ANN fuses the optic flow and horizon–based attitude to provide smooth attitude estimations. The results obtained from different parts of the research are tested and validated through simulations and real flight tests

Control-Oriented Reduced Order Modeling of Dipteran Flapping Flight

Flying insects achieve flight stabilization and control in a manner that requires only small, specialized neural structures to perform the essential components of sensing and feedback, achieving unparalleled levels of robust aerobatic flight on limited computational resources. An engineering mechanism to replicate these control strategies could provide a dramatic increase in the mobility of small scale aerial robotics, but a formal investigation has not yet yielded tools that both quantitatively and intuitively explain flapping wing flight as an "input-output" relationship. This work uses experimental and simulated measurements of insect flight to create reduced order flight dynamics models. The framework presented here creates models that are relevant for the study of control properties. The work begins with automated measurement of insect wing motions in free flight, which are then used to calculate flight forces via an empirically-derived aerodynamics model. When paired with rigid body dynamics and experimentally measured state feedback, both the bare airframe and closed loop systems may be analyzed using frequency domain system identification. Flight dynamics models describing maneuvering about hover and cruise conditions are presented for example fruit flies (Drosophila melanogaster) and blowflies (Calliphorids). The results show that biologically measured feedback paths are appropriate for flight stabilization and sexual dimorphism is only a minor factor in flight dynamics. A method of ranking kinematic control inputs to maximize maneuverability is also presented, showing that the volume of reachable configurations in state space can be dramatically increased due to appropriate choice of kinematic inputs

Science, technology and the future of small autonomous drones

We are witnessing the advent of a new era of robots — drones — that can autonomously fly in natural and man-made environments. These robots, often associated with defence applications, could have a major impact on civilian tasks, including transportation, communication, agriculture, disaster mitigation and environment preservation. Autonomous flight in confined spaces presents great scientific and technical challenges owing to the energetic cost of staying airborne and to the perceptual intelligence required to negotiate complex environments. We identify scientific and technological advances that are expected to translate, within appropriate regulatory frameworks, into pervasive use of autonomous drones for civilian applications

Vision-based control of near-obstacle flight

Lightweight micro unmanned aerial vehicles (micro-UAVs) capable of autonomous flight in natural and urban environments have a large potential for civil and commercial applications, including environmental monitoring, forest fire monitoring, homeland security, traffic monitoring, aerial imagery, mapping and search and rescue. Smaller micro-UAVs capable of flying inside houses or small indoor environments have further applications in the domain of surveillance, search and rescue and entertainment. These applications require the capability to fly near to the ground and amongst obstacles. Existing UAVs rely on GPS and AHRS (attitude heading reference system) to control their flight and are unable to detect and avoid obstacles. Active distance sensors such as radars or laser range finders could be used to measure distances to obstacles, but are typically too heavy and power-consuming to be embedded on lightweight systems. In this thesis, we draw inspiration from biology and explore alternative approaches to flight control that allow aircraft to fly near obstacles. We show that optic flow can be used on flying platforms to estimate the proximity of obstacles and propose a novel control strategy, called optiPilot, for vision-based near-obstacle flight. Thanks to optiPilot, we demonstrate for the first time autonomous near-obstacle flight of micro-UAVs, both indoor and outdoor, without relying on an AHRS nor external beacons such as GPS. The control strategy only requires a small series of optic flow sensors, two rate gyroscopes and an airspeed sensor. It can run on a tiny embedded microcontroller in realtime. Despite its simplicity, optiPilot is able to fully control the aircraft, including altitude regulation, attitude stabilisation, obstacle avoidance, landing and take-off. This parsimony, inherited from the biology of flying insects, contrasts with the complexity of the systems used so far for flight control while offering more capabilities. The results presented in this thesis contribute to a better understanding of the minimal requirements, in terms of sensing and control architecture, that enable animals and artificial systems to fly and bring closer to reality the perspective of using lightweight and inexpensive micro-UAV for civilian purposes