Samuel B. Lazarus

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 map-making 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 others 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.

Robust Covariance Estimation for Data Fusion From Multiple Sensors

This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics. This paper discusses the use of estimators using explicit measurements from the sensors involved versus estimators using only covariance estimates from the sensor models and navigation systems. Covariance intersection and a class of orthogonal Gnanadesikan-Kettenring estimators are compared using the 2-norm of the estimates. A Monte Carlo simulation of a typical mapping experiment leads to conclude that covariance estimation systems with a hybrid of the two estimators may yield the best results.

Autonomous sense & avoid capabilities based on aircraft performances estimation

An autonomous navigation system integrating both the path following and the autonomous sense & avoid functions is presented in this article. The sense & avoid algorithm was developed to provide an avoidance manoeuvre that ensures a minimum separation between the ownship and all other agents during its execution in a multiple flying threats scenario. The resolution manoeuvre is defined as step variations in the heading angle and altitude autopilots commands. The commands are optimised in order to get the smallest step command necessary to keep a minimum predefined separation between the ownship and the threats. Its computation is based on the estimation of the future trajectory of all the agents and, therefore, on the estimation of aircraft performance during the manoeuvre. The suggested resolution manoeuvre is updated at 1 Hz in order to take into account any unpredictable changes of the threat trajectories. The obtained heading and altitude change commands are displayed on a novel human–machine interface to support the pilot in the planning of the avoidance action. The proposed sense & avoid system is modelled in a Matlab/Simulink® environment for a Piper J3 Cub 40 model aircraft. The threats considered are aircrafts that communicate their states to the system through their Automatic Dependent Surveillance-Broadcast mode S transponders.

Airborne Vehicle Mapping of Curvilinear Objects Using 2-D Splinegon

This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor-based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter to track the obstacle and extract its map. The Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. The 2-D Splinegon technique is presented for the mapping of the unknown complex obstacles. This Splinegon technique, which is the most efficient and a robust way to estimate the boundary of a curved-nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice.

Multiple Threats Sense and Avoid Algorithm for Static and Dynamic Obstacles

This paper presents a new computationally efficient S&A algorithm for implementation in real-time applications for UAV. Based on a simplified optimisation approach, the proposed algorithm aims to provide a reliable resolution manoeuvre (horizontal and vertical) for multiple threat scenarios which include both air and ground threats/obstacles. In presence of a conflict risk, the avoidance manoeuvre is defined as step variation in the heading angle or altitude variation of the autopilots command. This step command is optimised in order to keep a minimum distance of separation between the ownship and all threats during the overall manoeuvre. The algorithm computes the separation distance between the UAV and the threats by calculating the future trajectories at each time step of both the ownship and the threat, while always taking into account the ownship performance envelope constraints. The algorithms were validated in simulation, where the ground threats were derived from the ground elevation maps, while for the aerial threats the aircraft communicate their flight data through an ADS-B mode S transponder. The resolution manoeuvre optimisation technique takes about 0.1 second to compute. Hence enabling the algorithm to cope with any rapid changes in the aerial threat trajectory.

Autonomous collision avoidance based on aircraft performances estimation

The paper describes a resolution manoeuvre definition algorithm for a Sense and Avoid (S&A) system, in which the avoidance manoeuvres are the step variations in the heading angle command of the Flight Path Control System (FPCS). The value of these commands is optimised in order to get the minimum step command value necessary to keep a minimum predefined separation between the ownship and the threat. The computation of the separation distance between the ownship and the threat is based on the estimation of the future trajectory. This estimation is obtained from the response of the linear model of the aircraft modified in order to get the results as close as possible to the nonlinear behaviour of the aircraft. The defined algorithm is tested through simulations in Matlab/Simulink® environment by considering some head-on conflict scenarios with a flying threat approaching from different directions. The threat considered is an aircraft that communicate its state to the system through its Automatic Dependent Surveillance-Broadcast (ADS-B) mode S transponder.

GPS/INS integration in a S&A algorithm based on aircraft performances estimation

In this paper the fusion between the data derived from the Global Positioning System (GPS) and the Inertial navigation System (INS) is integrated in a Sense And Avoid (S&A) system for Unmanned Aerial Vehicle (UAV) to increase its performances. The avoidance manoeuvre is defined in order to provide a minimum separation between the ownship and all the other agents during its overall execution in multiple flying threats scenarios. This is achieved by aiding the GPS measurements with the INS to estimate the ownship position using an Extended Kalman Filter (EKF) which uses a linear error model to estimate the errors in the INS states. The resolution manoeuvre are defined as step variations in the heading angle and altitude commands of the Flight Path Control System (FPCS). The value of these commands is optimised in order to get the minimum step command value necessary to keep a minimum predefined separation between the ownship and all the threats.

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

Abstract This article presents a cooperative guidance and estimation approach that enables multiple unmanned aerial vehicles (UAVs) to efficiently search and explore unknown environments. The computational efficient data information algorithm that leads to map building and update is based on geometrical tools known as two-dimensional splinegon. The circle packing search algorithm is used for the completeness of searching coverage of the exploring region, while the UAV trajectories are generated using Dubins path planning.

Airborne mapping of complex obstacles using 2D Splinegon

This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter and to track the obstacle so as to extract the map of the obstacle. This Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. This description takes into account the fact that the vehicle is made to fly around the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon technique. This splinegon technique, the most efficient and a robust way to estimate the boundary of a curved nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice.

Adaptive guidance for UAV based on Dubins path

Path planning is a complex problem, which involves meeting physical constraints of the UAVs, constraints from operating environment and other operational requirements. The foremost constraint to be met is that the paths must be flyable. Flyable paths are those that meet the kinematic constraints of the UAV. Satisfying this constraint ensures that the motion of the UAVs stays within the maximum bounds on manoeuvre curvature. The safety of the path is measured by ability of the path to avoid threats, obstacles, and other UAVs. In this paper an adaptive 3D path-following algorithm based on Dubins paths is presented. It is demonstrated efficient in different wind conditions, and with static obstacles.