Coordinated UAV Manoeuvring Flight Formation

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.

2003

21st Mediterranean Conference on Control and Automation, 2013

In this article we propose a hierarchical control structure for multi-agent systems. The main objective is to perform formation change manoeuvres, with guaranteed safe distance between each two vehicles throughout the whole mission, even if motion is restricted to a limited space and static obstacles are present. The key components that ensure safety are a robust control algorithm that is capable of stabilising the group of vehicles in a desired formation and a higher level path generation method that provides all the vehicles with safe paths, based on graph theoretic considerations. The method can efficiently handle a large group of any type of vehicles. As an illustration, the results are applied to a group of 18 quadrotor UAVs, where 2 of the UAVs cannot receive information from the others.

Operations Research, 2017

Unmanned aerial vehicles (UAVs) have been proved to be successful and efficient for information collection in a modern battlefield, especially in areas that are considered to be dangerous for human pilots. We propose a decentralized control strategy while requiring UAVs to maintain radio silence during the entire mission. The strategy is analyzed based on a scenario where a fleet of vehicles is assigned to search and collect uncertain information in a set of regions within a given mission time. We demonstrate that a region-sharing strategy is beneficial even when there is no extra reward gained from additional information collection. Implementing a region-sharing strategy requires solving a decentralized time allocation problem, which is computationally intractable. To overcome this, an approximate formulation is developed under an independence assumption for information collected by different vehicles. This approximate formulation allows us to decompose, by vehicle, the time allocation problem, and obtain an easily implementable policy that takes on a Markovian form. We develop a sufficient condition under which the approximate formulation becomes exact. A numerical study establishes the computational efficiency of the method.

2004

Inexpensive fixed wing UAVs are increasingly useful in remote sensing operations. They are a cheaper alternative to manned vehicles, and are ideally suited for dangerous or monotonous missions that would be inadvisable for a human pilot. Groups of UAVs are of special interest for their abilities to coordinate simultaneous coverage of large areas, or cooperate to achieve goals such as mapping. Cooperation and coordination in UAV groups also allows increasingly large numbers of aircraft to be operated by a single user. Specific applications under consideration for groups of cooperating UAVs are border patrol, search and rescue, surveillance, communications relaying, and mapping of hostile territory. The capabilities of small UAVs continue to grow with advances in wireless communications and computing power.

7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum, 2007

Proceedings of the Fifth European Conference on the Engineering of Computer-Based Systems, 2017

This paper presents a realtime, collision-free motion coordination and navigation system for an Unmanned Aerial Vehicle (UAV) fleet. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee a risk of a collision in realtime and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire fleet. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments. The results demonstrate that the proposed system successfully predicts and avoids all three kinds of collisions in realtime. Moreover, it generates efficient UAV routes, has an excellent runtime performance, efficiently scales to large-sized problem instances involving dozens of UAVs and obstacles, and is suitable for some densely populated, cluttered flying zones.

In this thesis we discuss a specific aspect of the cooperative control for teams of Unmanned Air Vehicles (UAV), namely, the dynamic reallocation of vehicles among teams executing concurrent operations. Our approach consists of a nominal planning problem and an execution control problem. Both planning and execution control are developed in mixedinitiative environments, where the operator has some degrees of freedom that allows him to tune the system’s behavior. These interactions gives the ability to the operator to react to contingencies of the mission that weren’t taken into account in the modelation of the world. The plan for each team consists of the minimum number of vehicles needed to execute a sequence of tasks with a given probability of success. Tasks are to be executed in an adversary environment, where the vehicles face the risk of being destroyed. The goal of execution control is to balance the performance of teams in order to increase robustness to several sources of uncertainty. The execution control is implemented using the framework of Stochastic Dynamic Programming (DP).

2007

This study focuses on the implementation and demonstration of the Real Time Path Planner (RTPP). It is an AI guidance system that was developed for an operational DoD unmanned aerial target control system. The RTPP is tested using the 6-DOF target simulator of Drone Formation Control System (DFCS). The RTPP uses the A* algorithm to generate the obstacle free routes. The data used for the A* graph, which represents the terrain map, was obtained from the National Elevation Dataset. UAV flight trajectories developed by this system undergo testing in a DoD flight simulator. An MQM-107D simulated drone is used for the testing. The partition size of the terrain map is varied within the study to obtain an optimal partition size. These tests analyze the flight trajectories produced by the RTPP, and the effect of varying the A* and the UAV parameters. This study demonstrates that the RTPP produces safe and maneuverable routes for the MQM-107D. Results are confirmed by time plots of key flight parameters recorded during the 6-DOF UAV simulation runs. The time plots show that the target did not have any difficulty flying the computer-generated pattern. It also shows that the target flew over benign terrain as predicted by the RTPP to reach its destination. This study demonstrates the innovative benefits and functionality added by Intelligent Systems theory to the real-time path planning and navigation task via the DFCS system. The RTPP works as designed, producing safe and flyable flight patterns for the drone.