Monocular vision is increasingly used in micro air vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicle movement with respect to the surroundings or sense changes in the environment. However, optical flow does not directly provide us the distance to an object or velocity, but the ratio of them. Thus, using optical flow in control involves nonlinearity problems which add complexity to the controller. To deal with that, we propose an algorithm that estimates distance and velocity of the vehicle based on optical flow measured from a monocular camera and the knowledge of control inputs. This algorithm applies an extended Kalman filter to state estimation and uses the estimates for landing control. We implement and test our algorithm in computer simulation and on board a Parrot AR.Drone 2.0 to demonstrate its feasibility for micro air vehicles landings. Results of the simulation and multiple flight tests show that the algorithm is able to estimate height and velocity of the micro air vehicles accurately, and achieves smooth landings with these estimates, even in windy outdoor environments.

A control strategy is proposed to deal with the fundamental gain selection problem of optical flow landings. It involves detecting the height by means of an oscillating movement and setting the control gains accordingly at the start of a landing. Then, during descent, the gains are reduced exponentially, with mechanisms in place to ensure high-performance landings. Real-world experiments with a quadrotor demonstrate successful landings in both indoor and outdoor environments.

In recent years, the identification of water leak detection methods has entered a wide range of fields. Pipeline failures in water distribution networks lead to the loss of a considerable amount of high-quality water. Different monitoring methods are often used to identify the failing infrastructure, which is subsequently maintained. Increased pressures on a fast-expanding water supply network needs the development of better leak detection technologies, particularly for use in smart building applications. This paper offers a detailed examination of water leak detection methods, intending to determine the state-of-the-art approaches and make recommendations for future research. It is designed to demonstrate smart buildings, but it may also be utilized in another similar context. This review concludes that, despite prior achievements, there is still much room for improvement, particularly in the domain of real-time models for earlier leak detection methods in building automation. These models should enable the integration of leakage detection, evaluation, and control system that, with minimal human interaction, may be customized for efficient leakage detection in real-world circumstances.

Micro Air Vehicles need to have a robust landing capability, especially when they operate outside line-of-sight. Autonomous landing requires the identification of a relatively flat landing surface that does not have too large an inclination. In this article, a vision algorithm is introduced that fits a second-order approximation to the optic flow field underlying the optic flow vectors in images from a bottom camera. The flow field provides information on the ventral flow ( V_x/h), the time-to-contact ( h/ – V_z), the flatness of the landing surface, and the surface slope. The algorithm is computationally efficient and since it regards the flow field as a whole, it is suitable for use during relatively fast maneuvers. The algorithm is subsequently tested on artificial image sequences, hand-held videos, and on the images made by a Parrot AR drone. In a preliminary robotic experiment, the AR drone uses the vision algorithm to determine when to land in a scenario where it flies off a stairs onto the flat floor.