Review Paper on Visual Control of Unmanned Aerial Vehicle for Handling Disaster Management Issue

In order to get the information of any vehicle which is travelling on the way we required to rely on Global Positioning System. The GPS provides the Global Positioning Information of vehicle, which relies on external source called satellites. But the satellite signal may get cut or have small signal strength in cluttered areas or is less reliable at low altitude areas. So I think of to use the Unmanned Aerial Vehicle, not only this by attaching the Rispberry pi which is a credit size single cheap computer in order to classify the object that we want. It is a multifunctional UAV which will monitor and classify the object along with it also perform the explosive detection. It plays a great role in disaster occurred areas in order to detect the human beings or finding the objects. It also plays a role in detecting the explosive material in a cluttered area. In a flooded area in order to monitor or detect the humans it is easy for UAV to move above the surface of the ground and water. And provides the detection as well as live streaming of the particular area and images can be captured by it. It has a sensor IED in order to detect the explosive material. It can provide the full image of the object by moving around the object. It is operated by remote control. At the transmitting side you can see the live streaming and images of the area on the display. You can also able to see the detected sign indicating object with a particular object

Symbolic Control and Planning of Robotic Motion [Grand Challenges of Robotics]

Mobile robots are complex systems that combine mechanical elements such as wheels and gears, electromechanical devices such as motors, clutches and brakes, digital circuits such as processors and smart sensors, and software programs such as embedded controllers. They have mechanical constraints (e.g., a car-like robot cannot move sideways), limited energy resources, and computation, sensing, and communication capabilities. They operate in environments cluttered with possibly moving and shape changing obstacles, and their objectives can change over time, such as in the case of appearing and disappearing targets. Robot motion planning and control is the problem of automatic construction of robot control strategies from task specifications given in high-level, human-like language. The challenge in this area is the development of computationally efficient frameworks allowing for systematic, provably correct, control design accommodating both the robot constraints and the complexity of the environment, while at the same time allowing for expressive task specifications

Development prediction algorithm of vehicle travel time based traffic data

This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera images and later distinguished vehicles are assigned to the looking at route segment so instantaneous and current velocities are calculated. All data were effectively processed and visualized using both MATLAB and Python programming language and its libraries

Modeling, Control and Design of a Quadrotor Platform for Indoor Environments

abstract: Unmanned aerial vehicles (UAVs) are widely used in many applications because of their small size, great mobility and hover performance. This has been a consequence of the fast development of electronics, cheap lightweight flight controllers for accurate positioning and cameras. This thesis describes modeling, control and design of an oblique-cross-quadcopter platform for indoor-environments. One contribution of the work was the design of a new printed-circuit-board (PCB) flight controller (called MARK3). Key features/capabilities are as follows: (1) a Teensy 3.2 microcontroller with 168MHz overclock –used for communications, full-state estimation and inner-outer loop hierarchical rate-angle-speed-position control, (2) an on-board MEMS inertial-measurement-unit (IMU) which includes an LSM303D (3DOF-accelerometer and magnetometer), an L3GD20 (3DOF-gyroscope) and a BMP180 (barometer) for attitude estimation (barometer/magnetometer not used), (3) 6 pulse-width-modulator (PWM) output pins supports up to 6 rotors (4) 8 PWM input pins support up to 8-channel 2.4 GHz transmitter/receiver for manual control, (5) 2 5V servo extension outputs for other requirements (e.g. gimbals), (6) 2 universal-asynchronous-receiver-transmitter (UART) serial ports - used by flight controller to process data from Xbee; can be used for accepting outer-loop position commands from NVIDIA TX2 (future work), (7) 1 I2C-serial-protocol two-wire port for additional modules (used to read data from IMU at 400 Hz), (8) a 20-pin port for Xbee telemetry module connection; permits Xbee transceiver on desktop PC to send position/attitude commands to Xbee transceiver on quadcopter. The quadcopter platform consists of the new MARK3 PCB Flight Controller, an ATG-250 carbon-fiber frame (250 mm), a DJI Snail propulsion-system (brushless-three-phase-motor, electronic-speed-controller (ESC) and propeller), an HTC VIVE Tracker and RadioLink R9DS 9-Channel 2.4GHz Receiver. This platform is completely compatible with the HTC VIVE Tracking System (HVTS) which has 7ms latency, submillimeter accuracy and a much lower price compared to other millimeter-level tracking systems. The thesis describes nonlinear and linear modeling of the quadcopter’s 6DOF rigid-body dynamics and brushless-motor-actuator dynamics. These are used for hierarchical-classical-control-law development near hover. The HVTS was used to demonstrate precision hover-control and path-following. Simulation and measured flight-data are shown to be similar. This work provides a foundation for future precision multi-quadcopter formation-flight-control.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Flight research capabilities of the NASA/Army rotor systems research aircraft

A description is given of the capabilities and limitations of the Rotor Systems Research Aircraft (RSRA) that was demonstrated during the development contract, and assesses the expected research capabilities of the RSRA on delivery to the government

A MATLAB-BASED GUI FOR REMOTE ELECTROOCULOGRAPHY VISUAL EXAMINATION

In this work, a MATLAB-based graphical user interface is proposed for the visual examination of several eye movements. The proposed solution is algorithm-based, which localizes the area of the eye movement, removes artifacts, and calculates the view trajectory in terms of direction and orb deviation. To compute the algorithm, a five-electrode configuration is needed. The goodness of the proposed MATLAB-based graphical user interface has been validated, at the Clinic of Child Neurology of University Hospital of Ostrava, through the EEG Wave Program, which was considered as “gold standard” test. The proposed solution can help physicians on studying cerebral diseases, or to be used for the development of human-machine interfaces useful for the improvement of the digital era that surrounds us today

Explicit non-linear model predictive control for autonomous helicopters

Trajectory tracking is a basic function required for autonomous helicopters, but it also poses challenges to control design due to the complexity of helicopter dynamics. This article introduces an explicit model predictive control (MPC) to solve this problem, which inherits the advantages of non-linear MPC but eliminates time-consuming online optimization. The explicit solution to the non-linear MPC problem is derived using Taylor expansion and exploiting the helicopter model. With the explicit MPC solution, the control signals can be calculated instantaneously to respond to the fast dynamics of helicopters and suppress disturbances immediately. On the other hand, the online optimization process can be removed from the MPC framework, which can accelerate the software development and simplify onboard hardware. Due to these advantages of the proposed method, the overall control framework has a low complexity and high reliability, and it is easy to deploy on small-scale helicopters. The proposed explicit non-linear MPC has been successfully validated in simulations and in actual flight tests using a Trex-250 small-scale helicopter