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The purpose of this paper is to describe optical flow‐based navigation of a very light fixed‐wing aircraft in flight between obstacles.

The optical flow information of two cameras mounted on the aircraft is used to detect the obstacle. It is assumed that the image processing has been completed and the optical flow vectors have been obtained beforehand. The optical flow is used to detect the obstacles and make a rapid turn manoeuvre for the aircraft.

It is shown that using the optical flow feedback by itself is unable to give a rapid turn to the aircraft and its rate should be employed into the control law. Six degree‐of‐freedom flight simulation showed that the proposed navigation and control strategy give satisfactory results in different flight environments like corridors with parallel and non‐parallel walls and in the L junctions. Simulations also showed that the aircraft flight velocity has little effect on collision avoidance performance.

This paper provides a theoretical framework to study the different parameters affect the obstacle detection and avoidance of an aircraft.

An analytical equation has been developed to relate the obstacle detection distance to the aircraft manoeuvrability parameters. In addition, an optical flow‐based controller also has been designed to provide rapid turn manoeuvres using the aileron control surface.

This paper seeks to develop a reliable and computationally efficient method for estimating and predicting large‐amplitude optical flows via taking into consideration their coherence along the time dimension.

Although the differential‐based techniques for estimating optical flows have long been in wide use owing to the relative simplicity of their mathematical description, their applicability is known to be limited to the situations, when the optical flow has a relatively small norm. In order to extend such method to deal with large‐amplitude optical flows, it is proposed to model the optical flow as a composition of its time‐delayed version and a complementary optical flow. The former is used to predict the current optical flow and, subsequently, to warp forward the preceding image of the tracking sequence, while the latter accounts for the residual displacements that are estimated using Kalman filtering based on the “small norm” assumption.

The study shows that taking into consideration the temporal coherence of optical flows results in considerable improvement in the quality of their estimation in the case when the amplitude of the optical flow is relatively large and, hence, the “small norm” assumption is not applicable.

In the present work, the algorithm is formulated under the assumption that the optical flow is affine. This assumption may be restrictive in practice. Consequently, an important direction to extend this work is to consider more general classes of optical flows.

The main contribution of the present study is the use of multigrid methods and a projection scheme to relate the state equation to the apparent image motion.

Flow measurement plays an important role in modern industrial production. Flow measurement methods based on optical fiber systems have become a main research focus.

Applying flow measurement theory and the structural characteristics of optical fiber, flow measurement of paste can be achieved through a combined laser Doppler and optical fiber sensing system based on the principle of optical fiber grating sensors and flow sensors. The system is developed to include light selection, photoelectric detection, a voltage amplifier circuit and a signal filtering circuit.

The system is shown, through a comparative experiment, to be of higher accuracy than the traditional ultrasonic method.

A new method for measuring the paste flow is presented, which is based on the principle of optical fiber Doppler. The method has the advantages of convenient installation, high accuracy and low cost. Experiments show that the method is feasible.

Owing to the technology growth, especially in Microsystems technology and Nanotechnology, new products will provide new ways to sense variables that are crucial for product improvement and system reliability. A big concern of the scientific community is the measurement of low level flow measurements, especially for the biomedical and/or systems on a chip approaches.Design/methodology/approach – A new flow meter concept design consists of a surface micromachined sensor having an optical high reflective mirror made of gold, which is attached to unique cantilever designs that bend due to the drag force of mass flow. The bending of the cantilevers produces the mirror to approach/depart from an optical fiber end‐tip. The reflective light to fiber is modulated using a Fabry‐Perot interferometry technique to determine the mirror separation to the fiber, which corresponds to the mass flow.Findings – The new concept design shows a big potential approach to measure low flow measurements for air, gas and liquids of low viscosity. The results of this concept, through finite element analysis, show that the material used to build the sensor, makes them excellent candidates for fabrication. The stresses of the materials and allowable (readable) bending are among the tolerances of such materials/construction‐design. The sensor is not affected by electromagnetic interference and does not require electrical currents to sense, i.e. it is perfectly suited for biomedical and low mass‐flow sensing such as lab‐on‐chip applications.Originality/value – Among all approaches to sense low flow measurements, most of them need either “big” turbine approaches (dimensions over 1 cm diameter), or the need of an electrical approach needed in the end measurement sensor. This work proposes a non‐electrical approach.

Sensor Review publishes the results of a major sensor survey.

Insects depend on the spatial, temporal and spectral distribution of light in the environment for navigation, collision avoidance and flight control. The principles of insect vision have been gradually revealed over the course of decades by biological scientists. The purpose of this paper is to report on bioinspired implementations and flight tests of these sensors and reflexes on unmanned aerial vehicles (UAVs). The devices are used for the stabilization of UAVs in attitude, heading and position. The implementations were developed to test the hypothesis that current understanding of insect optical flight control systems is feasible in real systems.

Design was based on behavioral and anatomical studies of insects. The approach taken was to test the designs in flight on a UAV.

The research showed that stabilization in attitude, heading and position is possible using the developed sensors.

Partial alternatives to magnetic, inertial and GPS sensing have been demonstrated. Optical flow and polarization compassing are particularly relevant to flight in urban environments and in planetary exploration.

For the first time the use of multispectral horizon sensing, polarization compassing and optical flow-based heading control have been demonstrated in flight.

The purpose of this paper is to establish analytical and numerical solutions of a navigational law to estimate displacements of hyper-static multi-legged mobile robots, which combines: monocular vision (optical flow of regional invariants) and legs dynamics.

In this study the authors propose a Euler-Lagrange equation that control legs’ joints to control robot's displacements. Robot's rotation and translational velocities are feedback by motion features of visual invariant descriptors. A general analytical solution of a derivative navigation law is proposed for hyper-static robots. The feedback is formulated with the local speed rate obtained from optical flow of visual regional invariants. The proposed formulation includes a data association algorithm aimed to correlate visual invariant descriptors detected in sequential images through monocular vision. The navigation law is constrained by a set of three kinematic equilibrium conditions for navigational scenarios: constant acceleration, constant velocity, and instantaneous acceleration.

The proposed data association method concerns local motions of multiple invariants (enhanced MSER) by minimizing the norm of multidimensional optical flow feature vectors. Kinematic measurements are used as observable arguments in the general dynamic control equation; while the legs joints dynamics model is used to formulate the controllable arguments.

The given analysis does not combine sensor data of any kind, but only monocular passive vision. The approach automatically detects environmental invariant descriptors with an enhanced version of the MSER method. Only optical flow vectors and robot's multi-leg dynamics are used to formulate descriptive rotational and translational motions for self-positioning.

Roger Main gives a four‐part report on the optical technologies which are playing an increasingly important role in sensor development.

Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.