Decentralized Triangular Guidance Algorithms for Formations of UAVs

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.

Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm

Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches.

How to Fly

Energy expenditure of flight can be reduced by flight formation (echelon, V-shape). Birds make use of this, as well as of upwash/downwash effects due to wing motion. Flight details were studied empirically by human-led migrations of the Northern bald ibis. The human leaders flew in light glider devices, the birds carried light GPS recorders on their backs.