Distributed selection of flight formation in UAV missions

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007

In military missions in hostile environments involving teams of UAVs flying in formation, it is important to get the maximum benefits of the auto-protection systems of each aircraft to enhance the global security and efficiency of the team. One way to achieve this is to select a proper configuration for the formation. In this paper, we present an approach to autonomously adapt the configuration of a formation and we focus on its evaluation within a realistic framework where each UAV is simulated independently and communicate through a network.

2007

A methodology is presented for real-time control of unmanned aerial vehicles (UAV) in the absence of apriori knowledge of location of sites in an inhospitable flight territory. Our proposed hostile control methodology generates a sequence of waypoints to be pursued on the way to the target. Waypoints are continually computed with new information about the nature of changing threat. The

Robotics

In this paper, we present a mixed-initiative motion control strategy for multiple quadrotor aerial vehicles. The proposed approach incorporates formation specifications and motion-planning commands as well as inputs by a human operator. More specifically, we consider a leader–follower aerial robotic system, which autonomously attains a specific geometrical formation, by regulating the distances among neighboring agents while avoiding inter-robot collisions. The desired formation is realized by a decentralized prescribed performance control strategy, resulting in a low computational complexity implementation with guaranteed robustness and accurate formation establishment. The multi-robot system is safely guided towards goal configurations, by employing a properly defined navigation function that provides appropriate motion commands to the leading vehicle, which is the only one that has knowledge of the workspace and the goal configurations. Additionally, the overall framework incorpo...

Annals of the New York Academy of Sciences, 2005

In recent years, formation flying has been recognized as an enabling technology for a variety of mission concepts in both the scientific and defense arenas. Examples of developing missions at NASA include MMS (Magnetospheric Multi-Scale), SIRA (Solar Imaging Radio Array), and TPF (Terrestrial Planet Finder). For each of these missions, a multiplesatellite approach is required in order to accomplish the large-scale geometries imposed by the science objectives. In addition, the paradigm shift of using a multiple-satellite cluster rather than a large, monolithic spacecraft has also been motivated by the expected benefits of increased robustness, greater flexibility, and reduced cost. However, the operational costs of monitoring and commanding a fleet of close-orbiting satellites is likely to be unreasonable unless the onboard software is sufficiently autonomous, robust, and scalable to large clusters.

Journal of Guidance, Control, and Dynamics, 2005

The design of a nonlinear controller to reconfigure a formation of a group of unmanned aerial vehicles (UAVs) is described. Reconfiguration of the formation might be needed to maintain the efficiency of the formation. Nonlinear six-degree-of-freedom, rigid-body, equations of motion developed in the virtual leader (VL)'s frame are used to model the UAVs in the formation. The formulation of the formation flight in VL frame enables the formationkeeping and formation reconfiguration to be treated in the same framework. The nonlinear equations of motion contain the wind effect terms and their time derivatives to represent the aerodynamic coupling involved in close formation flight. These wind terms are obtained by using an averaging technique that computes the effective induced wind components and wind gradients in the UAV's body frame. Dynamics of the engine and the actuators are also included in the study. An algorithm that generates a safe and feasible trajectory, given the current position and the position to go to, has been developed. A combination of integral control, optimal LQR design, and nonlinear state feedback linearization is used in the design of the position-tracking controller. Simulation results demonstrate that the controller is capable of producing a smooth reconfiguration without using the information of the vortex-induced wind effects on the follower UAV.

A fleet of UAVs flying according a planned mission in hostile environments must optimize its configuration, so that the UAVs auto-protection systems ensure the fleet safety. Such an optimization can be done in the mission planning phase, and must also be reactively updated to tackle unpredicted threats. In this paper, we present an approach to the problem of selecting autonomously the fleet configuration. Algorithms that explicitly consider models of the threats and of the countermeasures systems are presented, and integrated within a global decisional architecture that ensures reactiveness and constraints satisfactions.

2011

Today Unmanned Aerial Vehicles (UAVs) and in particular quad-rotors represent novel platforms to accomplish a wide set of missions as surveillance, Search & Rescue, inspection, photogrammetry. The main limitation of these vehicles is represented by the restricted operating area. The area is mainly limited by power supplies (batteries or fuel). A strategy to overcame this limitation is to increase the number of vehicles forming a coalition. The main benefit of coalition formation are the extended mission range and the capability to increase the sensorial set. Each vehicles is a part of a dynamic network that must be properly coordinated in order to optimize all the available resources. In this paper a new framework for simulation of unmanned vehicles in cooperative scenarios is first presented. The framework is based on the interaction of a physics-engine, which simulates the dynamics of vehicles and their interaction with world increasing the realism of simulation, and a simulation environment where the high-level strategy is designed/developed. A Model Predictive Control (MPC) is then introduced to solve the problem of leader-follower applied to quad-rotors. Using the developed framework and the MPC technique is possible to easily instantiate the coalition minimizing also a cost function. The obtained results from the control strategy point of view show that positioning error at steady state is equal to zero. The MPC allows also the modelling of different conflicting constraints as the control actions, positioning error, and fuel/energy consumption.

Proceedings of the 17th IFAC World Congress, 2008, 2008

Unmanned Aerial Vehicles (UAVs) became a technology that have attracted considerable interest in the commercial markets for the military and civilian uses, such as surveillance and reconnaissance, aerial surveys for natural sources, traffic monitoring, early forest fire detection etc. This paper deals with Nonlinear and Model Predictive Control (MPC) of Unmanned Aerial Vehicles (UAV) flying in formation. Although, UAVs present numerous advantages over the manned aircrafts, they face challenges in various aspects of control in autonomous mode, and even more, in formation flying. An advanced control system is demonstrated as a possible solution to improve and increase the level of the autonomous mode and flying capabilities of UAVs. This paper deals with advanced control system of Unmanned Aerial Vehicles (UAV) flying in formation.

2011

Unmanned Aerial Vehicles (UAVs) and in particular quad-rotors are gaining an increasing interest owing to their flexibility and versatility. Today the challenge is to integrate these versatile platforms in a wide fleet in order to perform cooperative tasks as surveillance, search & rescue, inspection. The coalition formation problem is a pre-condition for cooperative missions. This problem can be solved with different methodologies from biologically-inspired algorithms to parasocial consensus sampling ones. In this paper a new framework for simulation of unmanned vehicles in cooperative scenarios is first presented. Then a novel method that exploits the benefits of Model Predictive Control (MPC) for a coalition formation problem (leader-follower) is introduced. The obtained results evidence the good performance of MPC to solve the problem of coalition formation for unmanned aerial vehicles finding the optimal solution taking into account different kind of constraints. The developed framework allows also to easily change from simulated agent to real one.