UAVs Dynamic Mission Management in  Adversarial Environments

Faied, Mariam; Assanein, Ihemed; Girard, Anouck

doi:10.1155/2009/107214

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Recently, reinforcement learning (RL) [34], sometimes referred to as approximate dynamic programming (ADP) [35,36], has been investigated for use in differential games, such as air combat between two UAVs. In [37][38][39], various RL methods are used to obtain fast solutions as required in a tactically changing environment. In [37], where the mission management issues of UAVs in adversarial environments are investigated, a heuristic is developed to decouple the control commands and to reduce computational expense.…”

Section: Related Workmentioning

confidence: 99%

“…In [37][38][39], various RL methods are used to obtain fast solutions as required in a tactically changing environment. In [37], where the mission management issues of UAVs in adversarial environments are investigated, a heuristic is developed to decouple the control commands and to reduce computational expense. The work presented in [39] introduces hierarchically accelerated dynamic programming (HADP), which is an approximation method used to reduce computation time for dynamic programming; however, it only works effectively on finite state spaces and is difficult to adapt to infinite state spaces.…”

Section: Related Workmentioning

confidence: 99%

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Cooperative Path-Planning for Multi-Vehicle Systems

Wang

Phillips

2014

Electronics

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In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles' position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cooperative Path-Planning for Multi-Vehicle Systems

Wang

Phillips

2014

Electronics

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“…Military mission planning problems are often formulated as scheduling and assignment problems [9], [10]. Assignment and scheduling problems have originated in operations research where a large literature exists [8].…”

Section: A Literature Reviewmentioning

confidence: 99%

Communication-constrained distributed task assignment

Jackson

Faied

Kabamba

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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This paper considers the problem of distributed assignment of tasks to agents in the presence of task constraints, where the agents use a known, but arbitrary communication topology. The task assignment problem considered here requires that all agents that perform tasks related by a task constraint be able to communicate directly. The problem is motivated by complex military missions where tasks are assigned to various vehicles and tasks must be scheduled to meet constraints between them. This requires communication between vehicles responsible for tasks that are related by constraints. The physically distributed and dynamic nature of such missions combined with unreliable communication motivates algorithms that can perform the required distributed planning. Toward this goal, we introduce a method that assigns tasks under the restrictions imposed by these mission constraints. The new method presented here is a distributed search designed to solve a nonlinear, distributed constrained assignment problem for which a proof of correctness is presented. The method is illustrated on an example involving two unmanned air vehicles and two unmanned ground vehicles.

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“…The mixed integer linear programming formulation has been used to describe several types of UAV routing problems [14] and is a versatile tool, but scales exponentially with the number of tasks and agents. The branch and bound method has been used to solve complex UAV routing problems exactly [4]. Such representations can involve complex cost functions and environmental factors such as wind and adversarial action.…”

Section: Literature Reviewmentioning

confidence: 99%

A combined Tabu search and 2-opt heuristic for multiple vehicle routing

Jackson

Girard

Rasmussen³

et al. 2010

Proceedings of the 2010 American Control Conference

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In this paper, the authors consider the problem of assigning multiple agents to perform several tasks under restrictions limiting which agents are permitted to perform each task and restrictions dictating the order of completion of specific tasks. A unique solution description is introduced that allows for the direct satisfaction of these constraints. A two phase optimization procedure is used. A Tabu search method is used to optimize the task assignments and an exchange heuristic is used to refine the completion order of the tasks in the presence of precedence constraints.

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UAVs Dynamic Mission Management in Adversarial Environments

Cited by 10 publications

References 32 publications

Cooperative Path-Planning for Multi-Vehicle Systems

Cooperative Path-Planning for Multi-Vehicle Systems

Communication-constrained distributed task assignment

A combined Tabu search and 2-opt heuristic for multiple vehicle routing

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