Detection and estimation of moving obstacles for a UAV

In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles

This paper investigates the development of a new guidance algorithm for a formation of unmanned aerial vehicles. Using the new approach of bifurcating potential fields, it is shown that a formation of unmanned aerial vehicles can be successfully controlled such that verifiable autonomous patterns are achieved, with a simple parameter switch allowing for transitions between patterns. The key contribution that this paper presents is in the development of a new bounded bifurcating potential field that avoids saturating the vehicle actuators, which is essential for real or safety-critical applications. To demonstrate this, a guidance and control method is developed, based on a six-degreeof-freedom linearized aircraft model, showing that, in simulation, three-dimensional formation flight for a swarm of unmanned aerial vehicles can be achieved

Velocity field path-planning for single and multiple unmanned ariel vehicles

Unmanned aerial vehicles (UAV) have seen a rapid growth in utilisation for reconnaissance, mostly using single UAVs. However, future utilisation of UAVs for applications such as bistatic synthetic aperture radar and stereoscopic imaging, will require the use of multiple UAVs acting cooperatively to achieve mission goals. In addition, to de-skill the operation of UAVs for certain applications will require the migration of path-planning functions from the ground to the UAV. This paper details a computationally efficient algorithm to enable path-planning for single UAVs and to form and re-form UAV formations with active collision avoidance. The algorithm presented extends classical potential field methods used in other domains for the UAV path-planning problem. It is demonstrated that a range of tasks can be executed autonomously, allowing high level tasking of single and multiple UAVs in formation, with the formation commanded as a single entity

Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version