Unmanned aerial vehicle navigation and mapping

Abstract

The unmanned aerial vehicle (UAV) has significant advantages over the ground vehicle, where it can achieve a high degree of manoeuvrability, high speed response time and the ability of large area coverage. The problem considered here is that of an UAV localization and mapping using an extended Kalman filter (EKF), interval analysis (IA), covariance intersection (CI) and Hough transform (HT) for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are known to some extent. The vehicle is localized with respect to the known obstacles and to recognise the unknown obstacles to update the map of the environment. The focus is to develop an approach which can give a guaranteed performance of sensor-based localization and mapping that would increase the safety of the aerial vehicle and to produce a better performance in building the map of the environment. The guaranteed performance is quantified by explicit bounds of the position estimate of the vehicle. Generally, the UAVs carry the required sensors such as inertial sensors, accelerometers, and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and the mapmaking is carried out with time of flight sensors such as ultrasonic or laser sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. This task of data fusion is accomplished by combining the measurements from different sensors that are obtained from two different methods namely EKF and IA, and by processing these measurements with a data fusion algorithm using the CI principle. This fused information is used with the measurements from the time of flight sensors such as laser sensor to update the map of the environment by applying the HT technique. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components, which in turn, improves the accuracy and richness in updating the map of the partially known environment. This proposed intelligent sensor system can provide a mathematically provable performance guarantees that are achievable in practice.

Multi-modal target detection for autonomous wide area search and surveillance

“…Based on a search area specified interactively via the GCS, this way-point tasking is automatically derived as a search pattern incorporating additional constraints such as road/ground layout (UGV) and launch/recovery points (UAV). These search taskings are designed to maximize effective environment sampling given platform speed and sensor sample rate in relation to environment coverage using techniques such as [46]. Furthermore platforms can be re-tasked to resample given areas of the environment or to obtain additional confirmation of target presence with additional sensing capabilities.…”

Co-operative unmanned aerial vehicle searching and mapping of complex obstacles using two-dimensional splinegon

“…One such algorithm Fig. 3 The circle packing algorithm covers the plane by packing each circle of radius R c in the unknown environment that was reported in references [12] and [13] is implemented in this work so as to cover the whole area to perform the searching task. The problem here can be defined as one of finding the required number of circles of radius R c to completely cover the given area.…”

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations

“…2 The embedded GPS/INS system (EGI) is a navigation equipment widely used in aircrafts. [3][4][5] Nevertheless, vehicles flying at hypersonic velocities would be enveloped in a ''plasma sheath'' that prevents radio communication, telemetry and, most importantly, GPS signal reception for navigation. 6,7 The outage of GPS signals during the hypersonic and high dynamic cruise will affect the flight safety and control stability.…”