Optimal rendezvous trajectory for unmanned aerial-ground vehicles

Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low cost aerial surveillance and mapping applications in remote regions. There is however a key limitation, which is the fact that low cost UAVs have limited fuel capacity and hence require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in 3D taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low cost aerial surveillance and mapping applications in remote regions. There is however a key limitation, which is the fact that low cost UAVs have limited fuel capacity and hence require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in 3D taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach

Comacchio nell'alto medioevo. Il passaggio tra topografia e geoarcheologia

Il paesaggio antico del delta del Po è oggetto di studi di varia natura da diversi decenni. Potremmo azzardare, anzi, che questo territorio attiri l’attenzione dei ricercatori da secoli, come sembrano suggerire alcune carte settecentesche contenenti mirabili tentativi di ricostruzione del paesaggio di età romana e medievale sulla sola base delle fonti scritte. La presente ricerca ha fatto ricorso a metodi essenzialmente topografici come il telerilevamento, lo studio della cartografia storica, della toponomastica e delle fonti scritte. Tuttavia, nella convinzione che occuparsi di archeologia dei paesaggi significhi anche saper declinare il rapporto uomo-ambiente al livello del singolo sito, essa ha previsto il ricorso a discipline analitiche ma contemporaneamente microinvasive come la geoarcheologia. Con la collaborazione di specialisti di diversi settori, si è tentato, dunque, di sistematizzare da un lato quanto finora prodotto e, dall’altro, di creare nuove conoscenze su specifici ambiti paesaggistici

Constrained optimal motion planning for autonomous vehicles using PRONTO

\u3cp\u3eThis chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO, a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs), and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying.\u3c/p\u3

Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO, a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs), and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying

Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO , a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs) , and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying