Dongxu Li

Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control

Multiple Unmanned Aerospace Vehicle (UAV) resource allocation is one of the core steps to effectively exploit the capabilities of cooperative control of UAV teams. Although many multi -UAV resource allocation models can be found in the literature, relatively little has been said regarding soft weapon systems such as de coys and jammers, designed make the threat ineffective. In this paper, we formulate the resource allocation problem as an optimal assignment problem and present a new algorithm that is based on the principles of Particle Swarm Optimization (PSO). PSO follo ws a collaborative population based search, which models the social behavior of bird flocks and fish schools. PSO combines local search methods with global search, attempting to balance exploration and exploitation. We discuss the adaptation and implementa tion of the PSO search strategy to the resource allocation problem in the cooperative control of multiple UAVs. Simulation results indicate that the PSO based algorithm is a feasible approach for resource allocation problems in multiple UAV teams.

A Hierarchical Approach To Multi-Player Pursuit-Evasion Differential Games

The increasing use of unmanned assets and robots in modern military operations renews an interest in the study of general pursuit-evasion games involving multiple pursuers and multiple evaders. Due to the difficulty in formulation and rigorous treatment, the literature in this field is very limited. This paper presents a hierarchical approach to this kind of problem. With an additional structure imposed on decision-making of pursuers, this approach provides conservative guidance to pursuers by finding certain engagement between pursuers and evaders, and the saddle-point strategies are utilized by each pursuer in chasing the engaged evaders. A combinatorial optimization problem is formulated and scenarios are created to demonstrate the feasibility of the algorithm. This is a preliminary study on multi-player pursuit-evasion games and future directions are suggested.

Defending an Asset: A Linear Quadratic Game Approach

Techniques based on pursuit-evasion (PE) games are often applied in military operations of autonomous vehicles (AV) in the presence of mobile targets. Recently with increasing use of AVs, new scenarios emerge such as surveillance and persistent area denial. Compared with PE games, the actual roles of the pursuer and the evader have changed. In these emerging scenarios the evader acts as an intruder striking at some asset; at the same time the pursuer tries to destroy the intruder to protect the asset. Due to the presence of an asset, the PE game model with two sets of players (pursuers and evaders) is no longer adequate. We call this new problem a game of defending an asset(s) (DA). In this paper we study DA games under the framework of a linear quadratic (LQ) formulation. Three different DA games are addressed: 1) defending a stationary asset, 2) defending a moving asset with an arbitrary trajectory, and 3) defending an escaping asset. Equilibrium game strategies of the players are derived for each case. A repetitive scheme is proposed for implementation of the LQ strategies, and we demonstrate with simulations that the LQ strategies based on the repetitive implementation can provide good control guidance laws for DA games.

Team dynamics and tactics for mission planning

Mission planning is one of the core steps to effectively exploit the capabilities of multi-level cooperative control of multiple semi-autonomous entities, such as unmanned aerospace vehicles (UAVs). In this paper, we describe a methodology of Team Dynamics and Tactics (TDT) for mission planning in a military operation. This method for mission planning is implemented in a TDT module for an interconnected system called Strategies for Human-Automaton Resource Entity Deployment (SHARED). The main purpose of TDT is to develop and provide an effective target selection algorithm and an optimal salvo size algorithm to destroy the opposing force combat capabilities. Furthermore, the TDT mission plan will find an optimal assignment of decoys and avoid collateral damage. The proposed mission-planning scheme supplies the optimum degree of force for campaign objectives by using a linear integer programming with fuzzy objective function to allocate the best UAVs and weapons against each target and a parameter Nash game with proportional feedback control to determine optimum salvo size for each UAV. The effectiveness of the proposed scheme is illustrated by a Suppression of Enemy Air Defenses (SEAD) example, and is demonstrated in a simulation environment based on the Boeing C4ISim Open Experimentation Platform (Boeing OEP).

Information, decision-making and deception in games

Modeling deception in a real-world conflict situation is usually difficult. For a better understanding, we study deception through a fundamental relationship between information and decision-making. Under a probabilistic framework, we consider a zero-sum game with an asymmetrical structure, where player 1 receives additional information and player 2 has the potential to inject deception. We derive accuracy conditions on the information obtained by player 1, which can lead to a better decision. The feasibility of deception is further explored, which is conditioned on the quality of deceptive signals generated by player 2. We classify deception into passive and active deception.

Better cooperative control with limited look-ahead

This paper is an extension of the current planning methods used in UAV tactical decision-making areas. We propose a repetitive online optimization method with limited look-ahead intervals. A theoretical foundation is built and the condition of its applicability is given. Optimal solutions can be approached by iteration. The relationship of this method with other well-known control methods is described. Advantages and disadvantages are discussed with suggestions for possible remediation. Interesting examples are presented to demonstrate the strength of this method. Possible applications are suggested

Graph-based strategies for multi-player pursuit evasion games

Maximization of the second smallest eigenvalue of the graph Laplacian has recently been studied in the field of cooperative control. Instead of the second smallest eigenvalue, we design a gradient-based control law for multiple agents to maximize an arbitrary nonzero eigenvalue. The gradient of an eigenvalue is derived through a standard sensitivity analysis. Furthermore, connections are drawn between the connectivity control and pursuit-evasion (PE) problems with multiple players. A gradient-based strategy is designed and the performance is verified by simulations. A comparison with the previously designed suboptimal strategy is provided. This is a preliminary study of a graph theoretical approach to multi-player PE problems.

A Robust Hierarchical Approach To Multi-stage Task Allocation Under Uncertainty

A multi-stage task allocation problem is difficult to solve when the search space is large. The intrinsic uncertainty imbedded in military operations makes the problem more challenging. Scalability and robustness are recognized as two main issues. A new hierarchical algorithm is proposed to attack this problem. The algorithm has two levels. The upper level provides mutual coordination among all decision-makers; while the decision-making at the lower level is decentralized. The algorithm is not only a tasking planner but also an online feedback controller. Computational demand of the algorithm is divided into two parts. The most computationally intensive part can be implemented off-line, and the complexity of the online part is reduced significantly. Simulations show that this is potentially a good method for solving multi-stage resource allocation problems involving a large number of vehicles and tasks with robust performance.