Semi-Autonomous Navigation for Virtual Tactical Simulations in the Military Domain

Integrated constructive and virtual simulations are becoming popular for tactical training in the military domain. An important aspect concerning the integration of these simulation models in the construction of virtual tactical simulations is the modelling and implementation of different kinds of semi-autonomous agents. A fundamental feature of these agents is the capability of intelligently and realistically modelling task-oriented navigation activities in large virtual terrain simulation environments, while following underlying military doctrine and tactics. This paper reviews important navigation issues that emerge in such simulation systems and prominent Artificial Intelligence (AI) techniques that have been explored to solve them. From this analysis, a hybrid, semi-autonomous navigation framework is proposed aiming to fulfil the needs of virtual tactical training simulations, more specifically, in the military domain. As implemented in a system for the virtual tactical simulation of artillery battery tasks, the framework shows how to overcome the challenges of implementing realistic global and local navigation behaviours for military units and, at the same time, it shows that the semi-autonomous behaviours implemented are of primary importance to allow interaction with users for learning purposes in the simulation exercises.

[1]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[2]  Robert A. Sottilare,et al.  Visual modality research in virtual and mixed reality simulation , 2015 .

[3]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[4]  Nabil Sahli,et al.  Real-World Pathfinding using Agent-Based Simulation , 2005 .

[5]  Takashi Chikayama,et al.  Simulating the Collision Avoidance Behavior of Pedestrians , 2000 .

[6]  Milind Tambe,et al.  Intelligent Agents for Interactive Simulation Environments , 1995, AI Mag..

[7]  Alexander Dooms.  Parallel multi-agent path planning in dynamic environments for real-time applications , 2013 .

[8]  Johan Hagelbäck,et al.  Potential-field based navigation in StarCraft , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[9]  Craig W. Reynolds.  Steering Behaviors For Autonomous Characters , 1999 .

[10]  Stephen P. Murphy,et al.  Successes and failures in UK/US development of simulation , 2002, Simul. Pract. Theory.

[11]  Mubbasir Kapadia,et al.  Navigation and steering for autonomous virtual humans. , 2013, Wiley interdisciplinary reviews. Cognitive science.

[12]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[13]  Dinesh Manocha,et al.  Reciprocal Collision Avoidance and Multi-Agent Navigation for Video Games , 2012, MAPF@AAAI.

[14]  Luciana Porcher Nedel,et al.  Path-Planning for RTS Games Based on Potential Fields , 2010, MIG.

[15]  David Silver,et al.  Cooperative Pathfinding , 2005, AIIDE.

[16]  W. McGaghie,et al.  A critical review of simulation‐based medical education research: 2003–2009 , 2010, Medical education.

[17]  Andrew Fray.  Context Steering: Behavior-Driven Steering at the Macro Scale , 2015 .

[18]  R. R. Newell.  Art and science. , 1950, Stanford medical bulletin.

[19]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[20]  Stefan J. Johansson,et al.  The Rise of Potential Fields in Real Time Strategy Bots , 2008, AIIDE.

[21]  Raymond R. Hill,et al.  The art and science of live, virtual, and constructive simulation for test and analysis , 2014 .

[22]  Xiao Cui,et al.  A*-based Pathfinding in Modern Computer Games , 2011 .

[23]  Mohd Shahrizal Sunar,et al.  A Comprehensive Study on Pathfinding Techniques for Robotics and Video Games , 2015, Int. J. Comput. Games Technol..

[24]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[25]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.