Al Savvaris

A novel actuation concept for a multi rotor UAV

This paper proposes a novel strategy to improve the performance and fault tolerance of multi-rotor vehicles. The proposed strategy uses dual axis tilting propellers and thus enables three different actuation mechanisms, namely, gyroscopic torques, thrust vectoring and differential thrusting. Unlike the conventional quadrotor, the proposed strategy offers a wider range of control torques by combining the three actuation mechanisms. Conventional quadrotors cannot be reconfigured if one of rotors fails. However, the proposed strategy is still able to reconfigure the vehicle with complete failure of one rotor and a pair of adverse motors. In order to prove this concept, a dual axis tilting UAV is first designed and prototyped. Next, a mathematical representation of the prototyped vehicle is modelled and verified using experiments. Then, a control system is developed based on a PD controller and pseudoinverse control allocator and validated through tests on a rig and flight tests. The tests show that the vehicle is faster than a conventional counterpart and that it can resist the failure of two rotors. Finally, this paper suggests how to lead further substantial improvements in performance.

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.

Towards a fully autonomous swarm of unmanned aerial vehicles

With advances in UAS technologies the quadrotor was given a special interest for its manoeuvrability and payload capacity. These assets are amplified when more of them are deployed simultaneously in order to improve the situational awareness over areas of interest. As the number of agents operating in the same environment grows, a common intelligence is needed to optimize their cooperation and ensure their safety throughout the completion of the missions. This paper presents the results of experiments conducted to demonstrate a set of algorithms on a surveillance system employing a swarm of quadrotor UAVs to track detected targets. It was initially assumed that the UAV paths are generated at constant altitude to replace the complicated quadrotor dynamics by ones of a point mass entity. The system is then extended to the third dimension to allow for a more complex guidance and navigation scheme. Several simulations were performed under various circumstances to validate the accuracy and robustness of the system.

Energy management in swarm of Unmanned Aerial Vehicles

Automated maintenance has become a necessity for Unmanned Aerial Vehicle (UAV) systems to function in remote environments for an extended period of time with a higher number of vehicles. Once removed from the energy management loop, the human operator is free to concentrate on higher level task management and data analysis. This paper firstly describes the design, test and construction of an autonomous Ground Recharge Stations (GRS) for battery-powered quadrotor helicopter. In order to incorporate the charging of the quadrotors in the overall swarm behaviour, the focus of the research presented here has been to reduce the charging phase of a single vehicle by developing safer electrical contacts and using a balancer in the charging process. The amount of extra current available from the new design easily pushed the flying-time/charging-time ratio of the quadrotors over 1. The paper then describes a novel approach for the integration of this technology into an energy efficient multi-agent system. The development of a prioritisation function and queuing protocols between the UAVs and GRSs demonstrate an optimised solution to the assignment problem dependent on the mission profile. Numerical experiments show that the system’s energy management remains efficient regardless of number and position of the platforms, or nature of the environment.

RGB-D camera-based quadrotor navigation in GPS-denied and low light environments using known 3D markers

This paper presents an original approach for autonomous navigation based on RGB-D data and known 3D markers, where the basic concept is to detect and recognize the markers and then to use them for a straightforward pose estimation solution. The developed algorithms can allow a quadrotor to autonomously fly in (cooperative) GPS denied environments and/or when there is no natural or artificial illumination of the scene, by following a predetermined path consisting of successive targets having a well defined shape and/or color. Algorithms for target detection and recognition based on depth data are described which are optimized for real time use, paying particular attention to the on-board computational load. Experimental tests have been carried out by integrating a RGB-Depth sensor (ASUS Xtion Pro Live) on-board a custom-built quadrotor. First results confirm the potential of the proposed approach. The technique can be applied to different types of unmanned aerial vehicles (UAVs), as well as unmanned ground vehicles (UGVs).

LIDAR-inertial integration for UAV localization and mapping in complex environments

This paper presents customized techniques for autonomous localization and mapping of micro Unmanned Aerial Vehicles flying in complex environments, e.g. unexplored, full of obstacles, GPS challenging or denied. The proposed algorithms are aimed at 2D environments and are based on the integration of 3D data, i.e. point clouds acquired by means of a laser scanner (LIDAR), and inertial data given by a low cost Inertial Measurement Unit (IMU). Specifically, localization is performed by exploiting a scan matching approach based on a customized version of the Iterative Closest Point algorithm, while mapping is done by extracting robust line features from LIDAR measurements. A peculiarity of the line detection method is the use of the Principal Component Analysis which allows computational time saving with respect to traditional least squares techniques for line fitting. Performance of the proposed approaches is evaluated on real data acquired in indoor environments by means of an experimental setup including an UTM-30LX-EW 2D LIDAR, a Pixhawk IMU, and a Nitrogen board.

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.

Improved Gradient-Based Algorithm for Solving Aeroassisted Vehicle Trajectory Optimization Problems

The Space Maneuver Vehicles (SMV) [1, 2] will play an increasingly important role in the future exploration of space, since their on-orbit maneuverability can greatly increase the operational flexibility and are more difficult as a target to be tracked and intercepted. Therefore, a well-designed trajectory, particularly in skip entry phase, is a key for stable flight and for improved guidance control of the vehicle [3, 4]. Trajectory design for space vehicles can be treated as an optimal control problem. Due to the high nonlinear characteristics and strict path constraints of the problem, direct methods are usually applied to calculate the optimal trajectories, such as direct multiple shooting method [5], direct collocation method [5, 6], or hp-adaptive pseudospectral method [7, 8]. Nevertheless, all the direct methods aim to transcribe the continuous-time optimal control problems to a Nonlinear Programming Problem (NLP). The resulting NLP can be solved numerically by well-developed algorithms such as Sequential Quadratic Programming (SQP) and Interior Point method (IP) [9, 10]. SQP methods are used successfully for the solution of large scale NLPs. Each Newton iteration of the SQP requires the solution of a quadratic programming subproblem

Development of a fuel cell hybrid-powered unmanned aerial vehicle

This paper describes the design and development of a hybrid fuel cell/battery propulsion system for a long endurance small UAV. The high level system architecture is presented, followed by the hardware-in-the-loop testing and performance analysis. A high fidelity 6-DoF simulation model of the complete system was developed and used to test the system under different battery state-of-charge. The simulation model included the power manager for the hybrid propulsion system configuration, which is based on rule-based control. The simulation results are compared with the experimental results obtained from the Hardware-in-the-Loop testing.