Team dynamics and tactics for mission planning

Abstract

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).

A Markov game theoretic data fusion approach for cyber situational awareness

“…That is, they are not just passively static but might purposefully take actions to help one side in a battle to minimize their losses or achieve some political purpose. Unfortunately, existing game theoretic models usually do not consider this situation, although collateral damage has been considered in a paper on a two-player game model by Cruz et al [6]. In this research, a three-player dynamic game model is formulated, in which two opposing forces and one normal player that might be either neutral or slightly biased [7].…”

Role of cooperation in coupling game theory

“…If such game structure is applied on a cooperative background (Cruz et al 2003), considering as x a and as x b , Theorem 1 can be directly applied. In some cases, the range of the new equilibrium in rational scenario can be determined.…”

“…In Cruz et al (2003), sub-optimal game strategies were proposed for the salvo sizes of UAV teams, in which there are to-be-determined constant parameters, say, chosen by Team A and chosen by Team B. After applying that strategy, the overall payoff functions for blue and red will be functions of and as stated in (29)…”

UAVs Dynamic Mission Management in Adversarial Environments

“…Other approaches to supervision and control of multiple UAVs are considered in [28,29]. However, only a small number of papers [9,[30][31][32] to date address the problem of dynamic teams whose composition may change at run time and of team supervision in this context.…”

“…Collaboration of multiple UAVs in complicated environments is also an area of wide scope and great interest, including planning, scheduling, and resource allocation for multiUAV, multitask missions [1][2][3][4], coordinated area surveillance [5,6], collision avoidance [7], etc. New concepts of operation are emerging for UAVs in SEAD mission, as described in [8][9][10][11], and facilitated by the Interactive Warfare Simulation (IWARS) [8], Boeing C4ISim Open Experimentation platform (Boeing OEP) [9], and Flexible Analysis Modeling and Exercise System (FLAMES) software [11]. A number of hardware platforms [12][13][14][15][16] are available for those wishing to validate their ideas experimentally.…”