This paper presents a custom designed in-pipe inspection robot that is developed for a pipe of diameter 0.203 m, commonly found in the oil and gas industry. Several pressure sensors are incorporated on board the robot, which are used for detecting leaks. The robot has a propeller arrangement, which not only drives the robot forwards, but also helps simulate a flow in an empty pipe, and thus aids detection of leaks. Further, the leak detection system is augmented by a neural network based verification framework, which improves the robustness of leak detection by allowing the operator to check their identification of a leak by passing it through a neural network based system. This paper presents the details of construction of the actual robot, and presents experimental data which shows successful neuralnetworks based detection of leaks in various scenarios.

This article develops a prototype quadcopter drone-based system for inspection of power-line ceramic insulators. The drone uses its onboard cameras and Raspberry Pi single-board computer to monitor the health condition of outdoor ceramic insulators. The main contribution of this article is the development of a complete quadcopter-based system prototype for overhead power-line ceramic insulators inspection. The system is capable of performing the required computer vision routines for insulator health monitoring, either onboard or on an onshore ground station computer. In the onshore mode of operation, the drone captures images as it flies and simultaneously sends them to the onshore ground station. The developed system is tested in real life on a small-scale model frame, on which insulators are mounted. The results presented in this article show that quadcopter-based insulator inspection can be carried out successfully using both onshore and onboard computer vision techniques, with acceptable quality in terms of precision and computer vision time.

Recently, quadrotor-based drones have attracted a lot of attention because of their versatility, which makes them an ideal medium for a variety of applications, e.g., personal photography, surveillance, and the delivery of lightweight packages. The flight duration of a drone is limited by its battery capacity. Increasing the payload capacity of a drone requires more current to be supplied by the battery onboard a drone. Elevated currents through a Li-ion battery can increase the battery temperature, thus posing a significant risk of fire or explosion. Li-ion batteries are suited for drone applications, due to their high energy density. There have been attempts to use hydrogen fuel cells onboard drones. Fuel cell stacks and fuel tank assemblies can have a high energy to weight ratio. So, they may be able to power long duration drone flights, but such fuel cell stacks and associated systems, are usually extremely expensive. Hence, this work proposes the novel use of a less expensive, low capacity, metal hydride fuel stick-powered fuel cell stack as an auxiliary power supply onboard a drone. A primary advantage of this is that the fuel sticks can be used to cool the batteries, and a side effect is that this slightly reduces the burden on the onboard Li-ion battery and provides a small increment in flight time. This work presents the results of an experimental study which shows the primary effect (i.e., decrease in battery temperature) and the secondary side effect (i.e., a small increment in flight time) obtained by using a fuel cell stack. In this work, a metal hydride fuel stick powered hydrogen fuel cell is used along with a Li-ion battery onboard a drone.

This paper presents a robust state space sinusoidal reference tracking controller that is able to estimate the overall friction faced by the platform attached to a linear voice coil motor. The system proposed is able to estimate overall friction faced by the platform attached and is also able to compensate for it. This paper discusses the various simple model of friction found in literature and uses them as a basis for the importance of friction compensation. Further analysis is provided on the formation of the sinusoidal reference tracking controller which is followed by the overall augmented system. A practical implementation of the systems is also provided which is used to obtain the results for the friction estimation. The results found from the implementation of the system are compared to the free-fall method used to estimate the Coulomb and viscous friction parameters.

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.

This letter presents the design of an optimal sinusoidal tracking and disturbance rejection controller for a linear voice-coil motor. The optimal tracking and disturbance rejection system is used to characterize the nonlinear dynamic friction of the servo motor. The control scheme allows investigating the hysteresis characteristics of friction as a function of frequency. Experimental results indicate the effectiveness of the proposed identification procedure for the dynamic friction estimation.