Three-dimensional formation flying using bifurcating potential fields

This paper describes the design of a three-dimensional formation flying guidance and control algorithm for a swarm of autonomous Unmanned Aerial Vehicles (UAVs), using the new approach of bifurcating artificial potential fields. We consider a decentralized control methodology that can create verifiable swarming patterns, which guarantee obstacle and vehicle collision avoidance. Based on a steering and repulsive potential field the algorithm supports flight that can transition between different formation patterns by way of a simple parameter change. The algorithm is applied to linear longitudinal and lateral models of a UAV. An experimental system to demonstrate formation flying is also developed to verify the validity of the proposed control system

Verifiable control of a swarm of unmanned aerial vehicles

This article considers the distributed control of a swarm of unmanned aerial vehicles (UAVs) investigating autonomous pattern formation and reconfigurability. A behaviour-based approach to formation control is considered with a velocity field control algorithm developed through bifurcating potential fields. This new approach extends previous research into pattern formation using potential field theory by considering the use of bifurcation theory as a means of reconfiguring a swarm pattern through a free parameter change. The advantage of this kind of system is that it is extremely robust to individual failures, is scalpable, and also flexible. The potential field consists of a steering and repulsive term with the bifurcation of the steering potential resulting in a change of the swarm pattern. The repulsive potential ensures collision avoidance and an equally spaced final formation. The stability of the system is demonstrated to ensure that desired behaviours always occur, assuming that at large separation distances the repulsive potential can be neglected through a scale separation that exists between the steering and repulsive potential. The control laws developed are applied to a formation of ten UAVs using a velocity field tracking approach, where it is shown numerically that desired patterns can be formed safely ensuring collision avoidance

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

3D Formation Control in Multi-Robot Teams Using Artificial Potential Fields

Multi-robot teams find applications in emergency response, search and rescue operations, convoy support and many more. Teams of autonomous aerial vehicles can also be used to protect a cargo of airplanes by surrounding them in some geometric shape. This research develops a control algorithm to attract UAVs to one or a set of bounded geometric shapes while avoiding collisions, re-configuring in the event of departure or addition of UAVs and maneuvering in mission space while retaining the configuration. Using potential field theory, weighted vector fields are described to attract UAVs to a desired formation. In order to achieve this, three vector fields are defined: one attracts UAVs located outside the formation towards bounded geometric shape; one pushes them away from the center towards the desired region and the third controls collision avoidance and dispersion of UAVs within the formation. The result is a control algorithm that is theoretically justified and verified using MATLAB which generates velocity vectors to attract UAVs to a loose formation and maneuver in the mission space while remaining in formation. This approach efficiently scales to different team sizes

UAV group formation collision avoidance method based on second-order consensus algorithm and improved artificial potential field

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the "leader-follower" strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method

Decentralized Control of a Swarm of Unmanned Aerial Vehicles

Molti operatori controllano un solo Unmanned Aerial Vehicle -- Veicolo Aereo Non Equipaggiato -- rendendo il sistema di controllo non scalabile. Attualmente, nell'ambito del controllo di questo tipo di veicoli, la tendenza \'e quella di gestire un gruppo di Unmanned Aerial Vehicle tramite un solo operatore in modo da avere un sistema in grado di operare con migliaia di Unmanned Aerial Vehicle che volano sopra una nazione. Swarm Intelligence, basata sui cosiddetti insetti sociali, fornisce le linee guida per progettare sistemi decentralizzati. In particolare, gli insetti sociali sono in grado di perseguire diversi obiettivi, dalla costruzione e difesa del nido, alla ricerca del cibo, al prendersi cura del nido, all'assegnazione di squadre di operai, alla costruzione di ponti. Questa tesi presenta un framework per il controllo decentralizzato di uno sciame di Unmanned Aerial Vehicle basato su funzioni di potentiale artificiale caratterizzate da propriet\'a attrattive e repulsive, che sono usate rispettivamente per raggiungere l'obiettivo e per evitare le eventuali collisioni. Ciascun veicolo dello sciame utilizza un numero limitato di informazioni degli altri veicoli, ed inoltre \'e caratterizzato come un agente con dinamica molto semplice. In questo schema, pi\'u agenti di uno sciame sono in grado di raggiungere una configurazione e di mantenerla, mentre migrano come gruppo ed evitano collisioni tra di loro. Pertanto, i comportamenti del sistema a sciame proposto in questa tesi sono la configurazione e la migrazione del gruppo, e includono la elusione di collisioni. In particolare, questa tesi analizza diverse espressioni di potenziale per determinare in quanto tempo lo sciame converge alla direzione e velocit\'a desiderata, e quanto \'e capace lo sciame ad evitare le collisioni tra gli agenti. Inoltre, sono state determinate due metriche che forniscono la stima del migliore potenziale in un determinato scenario. Una metrica quantifica quanto velocemente lo sciame converge ad una data velocit\'a, e la seconda analizza quanto robusto \'e il potenziale per evitare le collisioni. Le simulazioni mostrano che la soluzione proposta permette di costruire un sistema a sciame in grado di gestire la migrazione e la configurazione del gruppo in presenza di ostacoli utilizzando un numero limitato di informazioni