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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.
Advances in Intelligent Systems and Computing, 2020
A system of cooperative unmanned aerial vehicles (UAVs) is a group of agents interacting with each other and the surrounding environment to achieve a specific task. In contrast with a single UAV, UAV swarms are expected to benefit efficiency, flexibility, accuracy, robustness, and reliability. However, the provision of external communications potentially exposes them to an additional layer of faults, failures, uncertainties, and cyber-attacks and can contribute to the propagation of error from one component to other components in a network. Also, other challenges such as complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion should be addressed adequately. Main applications of cooperative UAVs are border patrol; search and rescue; surveillance; mapping; military. Challenges to be addressed in decision and control in cooperative systems may include the complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion. In this paper, emerging topics in the field of cooperative UAVs control and their associated practical approaches are reviewed.
IEEE Control Systems Magazine, 2010
roadly considered, the fi eld of cooperative decision and control covers those interdisciplinary methods that can be used for operating of semiautonomous agents deployed to achieve a common objective. By exploiting the agents' capabilities, it is expected that the combined effort of the team can exceed the sum of its parts. Harnessing this potential benefi t, however, is challenging due to the complexity that dealing with miscellaneous components in dynamic and uncertain environments brings about.
2004
Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (U.A.V.s), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-theloop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.
2007
The Defence Science & Technology Organisation (DSTO), which is part of the Australian Department of Defence, is developing a research capability that uses small, inexpensive, autonomous uninhabited air vehicles (UAVs) to detect, identify, target, track, and electronically engage ground-based targets such as radars. The UAVs, which act autonomously and cooperatively, use a geographically distributed and heterogenous mix of relatively unsophisticated electronic warfare (EW) sensors and other miniaturised payloads networked together to deliver a distributed situational awareness picture that can be shared across the command echelons. If the many design challenges are overcome, the cooperation and networking of these platforms and payloads could provide results superior to those of the significantly more expensive, platform-centric systems, but with the added advantage of robustness. This paper outlines the challenges relating to autonomy, supervision, and control that the developers face and reports on the development of DSTO's multi-UAV cooperative to date.
MDPI, 2022
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Frontiers in Robotics and AI, 2022
Editorial on the Research Topic Control of Cooperative Drones and Their Applications Cooperative drones alleviate the burden of a single drone to perform an assigned task, just like how humans cooperate and help each other towards a common goal. A depiction of drone cooperation is shown in Figure 1. They can afford faster execution, bigger coverage, larger payload, shared resources, and other increased capabilities not available in a single drone. However, this comes with a price in terms of control complexity, communication requirements, increased points of failure, cost of system components, and computational load. This Research Topic aims to present recent state-of-the-art solutions to pressing challenges faced by drones when cooperating to perform their common goal. These challenges can be critically understood when the solutions are focused on the common goal of cooperation which can be divided into three categories: shared load, shared task, and shared resource. The categories have overlaps but these classifications take on the highest priority goal of cooperation. Shared load drone cooperation also includes cooperative manipulation, such that it encompasses drone cooperation that has direct contact with its environment. Most noteworthy are flying arms, which can be composed of dual-arms or more arms having "winged shoulders" capable of aerial dual-arm manipulation or several arms manipulation. This may revolutionize cooperative manipulation by the increased dimension of the workspace. In some cases, the mechanism attached to the drones may not be an arm, but just a gripper making the drone capable of grabbing objects. In this case, the task can be a simple cooperative pick-and-place or a cooperative perching of two or more drones to rest. Shared task drone cooperation encompasses drone cooperation with no direct contact with the environment, but with a specific task to complete as a group. This includes increased area coverage in mapping, target tracking in large areas, target tracking with tracking size greater than individual drone coverage, multiple drone pesticide sprayers in agriculture, swarm drone light display, combating fire cooperatively, and many others. This type of drone cooperation is the most common and is expected to further increase significantly. In principle, the task capability of a single drone is simply multiplied such
This work develops a novel distributed algorithm for task assignment (TA), coordination and communication of multiple UAVs engaging multiple targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for distributed flight coordination. Distributed TA is implemented by a relaxation process, wherein each node computes a temporary TA based on the union of the TAs of its neighbors in the tree. The computation of the temporary TAs at each node is based on weighted matching in the UAV-target distances graph. A randomized sampling mechanism is used to propagate TAs among different parts of the tree. Thus, changes in the location of the UAVs and targets do not pass through the root of the tree. Simulation experiments show that the combination of the flocking and the TA algorithms yields the best performance.
2013
Recently, unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UASs) have gained significant attention and their integration to everyday life is one of the most actively investigated problem in numerous countries. These vehicles can perform various challenging tasks efficiently, either alone or in cooperation with other similar vehicles. However, numerous open questions exist in this field of research due to the versatility of applications and the emerging problems. This work focuses on control related problems that include single vehicles and vehicle groups in indoor environment, while state measurement and estimation are also of importance. The research has been made available by the quadrotor helicopter research project initiated by the Department of Control Engineering and Information Technology of the Budapest University of Technology and Economics (BME IIT) and the Systems and Control Lab of the Institute for Computer Science and Control of the Hungarian Academy of S...
2008
This study develops a novel distributed algorithm for task assignment (TA), coordination, and communication of multiple unmanned aerial vehicles (UAVs) engaging multiple targets and conceives an ad hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for distributed flight coordination. Distributed TA is implemented by a relaxation process, wherein each node computes a temporary TA based on the union of the TAs of its neighbors in the tree. The computation of the temporary TAs at each node is based on weighted matching in the UAV-target distances graph. A randomized sampling mechanism is used to propagate TAs among different parts of the tree. Thus, changes in the location of the UAVs and targets do not pass through the root of the tree. Simulation experiments show that the combination of the flocking and the TA algorithms yields the best performance.
AIAA 3rd "Unmanned Unlimited" Technical Conference, Workshop and Exhibit, 2004
This paper discusses the development and testing of a unique testbed consisting of a fleet of eight autonomous unmanned aerial vehicles (UAVs) that was designed as a platform for evaluating autonomous coordination and control algorithms. Future UAV teams will have to autonomously demonstrate cooperative behaviors in dynamic and uncertain environments, and this testbed can be used to compare various control approaches to accomplish these coordinated missions. A hierarchical configuration of task assignment, trajectory design, and low-level, waypoint following, are used in a receding horizon framework to control the UAV team. Numerous trajectory optimization and team coordination algorithms have recently been developed to execute these UAV missions. This paper highlights several of these algorithms and presents typical results for representative experiments. These demonstrations of the high-level planning algorithms on scaled vehicles operating in uncertain and dynamic environments represent key steps towards transitioning them to future UAV missions. * Associate Professor,
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).
Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (U.A.V.s), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-theloop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.
IEEE Transactions on Intelligent Transportation Systems
The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.
Encyclopedia of Aerospace Engineering, 2015
In the first part of this section, the concepts of coordination and cooperation are briefly presented due to their relevance in any system with multiple autonomous vehicles. Then, a classification based on the coupling between the vehicles is outlined. 2.1 Coordination and Cooperation In platforms involving multiple vehicles, the concepts of coordination and cooperation play an important role. In
2007
A platoon formation of autonomous vehicles refers to a set of spatially distributed vehicles whose dynamic states are coupled through a common, cooperative control law. We present a technical approach and prototype of a cooperative controller for unmanned aerial vehicles (UAVs) and other autonomous robot vehicles. We presented the underlying theory for the decentralized and cooperative control in [10]. The present paper focuses on the next development step, i.e. a prototype system. We first review the cooperative controls concept and then discuss disturbance rejection, and path planning and tracking in a cooperative controls context. Finally the prototype design and its hierarchical controls architecture are presented, as well as the high-level software implementation.
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
Journal of Intelligent and Robotic Systems, 2017
This article presents the development of an autonomous and distributed movement coordination algorithm for Unmanned Aerial Vehicles (UAVs) swarms used in communication relay networks and in exploratory area surveillance missions. This work studies the performance of a hybrid algorithm combining pheromone maps, market auction paradigms and proactive link maintenance mechanisms to create a self-organizing flying network capable of providing network support for the UAV nodes already engaged in exploration and targeting tasks in the surveillance missions. In order to validate the proposal, simulations were performed assessing the desired performance aspects related to the target allocation and network connectivity. The acquired results provide evidence that the proposed solution is able to maintain the balance between the performance goals.
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
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