Visualization Optimization: Application to the RoboCup Rescue Domain 2 3 Pedro Miguel Moreira1,4, Luis Paulo Reis ,4 and A. Augusto de Sousa ,4 1ESTG-IPVC - Escola Superior de Tecnologia e Gesrao de Viana do Castelo, Portugal 2LlACC - Laboratorio de Inteligencia Artificial e Ciencia de Computadores da Univ. Porto, Portugal 3INESC-Porto - Instituto de Engenharia de Sistemas e Computadores do Porto, Portugal 4FEUP - Faculdade de Engenharia da Universidade do Porto, Portugal Abstract In this paper we demonstrate the use of intelligent optimization methodologies on the visualization optimization of virtual/simulated environments. The problem of automatic selection of an optimized set of views, which better describes an on-going simulation over a virtual environment is addressed in the context of the RoboCup Rescue Simulation domain. A generic architecture for optimization is proposed and described. We outline the possible extensions of this architecture and argue on how several problems within the fields of Interactive Rendering and Visualization can benefitfrom it. Categories and Subject Descriptors (according to ACM CCS): 1.3.7 [Computer Graphics]: 1.3.7 Virtual reality 1.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search Additional Info corresponding author: [email protected] Submitted to: SIACG 2006 Ibero-American Symposium on Computer Graphics 2006, Santiago de Compostela, Spain, July 2006. Visualization Optimization: Application to the RoboCup Rescue Domain l 2 3 Pedro Miguel Moreira ,4t, Luis Paulo Reis ,4:j: and A. Augusto de Sousa ,4§ I ESTG-IPVC - Escola Superior de Tecnologia e Gestao de Viana do Castelo, Portugal 2LIACC - Laborat6rio de Inteligencia Artificial e Ciencia de Computadores da Univ. Porto, Portugal 3INESC-Porto - Instituto de Engenharia de Sistemas e Computadores do Porto, Portugal 4FEUP - Faculdade de Engenharia da Universidade do Porto, Portugal Abstract In this paper we demonstrate the use ofintelligent optimization methodologies on the visualization optimization ofvirtual/simulated environments. The problem ofautomatic selection ofan optimized set ofviews, which better describes an on-going simulation over a virtual environment is addressed in the context ofthe RoboCup Rescue Simulation domain. A generic architecture for optimization is proposed and described. We outline the possible extensions ofthis architecture and argue on how several problems within the fields ofInteractive Rendering and Visualization can benefit from it. Categories and Subject Descriptors (according to ACM CCS): 1.3.7 [Computer Graphics): 1.3.7 Virtual reality 1.2.8 [Artificial Intelligence): Problem Solving, Control Methods, and Search 1. Introd uction ally feasible due to the inherent problem complexity and to time constraints. Thus, meta-heuristics are used tollnd a best In this paper we address the problem of automatically find (suboptimal) solution. a fixed set of views (multi-view) over a three dimensional dynamical and evolving simulated environment that gives to the user a good representation. We report the application In our approach we propose an optimization architecture to the RoboCup Rescue Domain where we aim at obtain­ relying on an optimization agent that works autonomously ing, at each moment, the set of views which better describe from the main visualization / rendering application. Our ob­ the emergency situations and rescue operations. These multi­ jective is to develop the proposed architecture in order to view should provide the user with useful information leading apply it on other problems with minor effort. to a correct understanding of the whole environment. The problem offinding the best set of views over a scene The optimization agent provides a set use of intelligent can be stated as an optimization problem. This is an in­ optimization techniques [PKOO], such as (but not restricted teresting and useful problem which relates to other prob­ to): genetic algorithms, tabu search, simulated annealing, or lems such as : automatic object [VS03] and scene explo­ neural networks to efficiently find optimized solutions. ration [AVF04], virtual camera motion [MCOO], virtual cinematography [DZ95] and automatic selection of images The rest of the paper is organized as follows. Following to Image-Based Modeling and Rendering (IBM&R) sys­ this introduction, Section 2 describes the aplication domain tems [VFSH03]. Finding the optimal solution is not usu- - Robocup Rescue, where reported experiments were con­ ducted. Next, in Section 3, we detail and formalize the ad­ dressed problem. Our proposed methodology is presented in t [email protected] Section 4. In Section 5 experimental results are presented :j: [email protected] and discussed. Finally, in Section 6, conclusions and outlines § [email protected] ofour on going and future work are presented. o P. M. Moreira, L. P. Reis & A, A, de Sousa / Visualization Optimization Application to the RoboCup Rescue Domain 2. RoboCup rescue domain in this area, but the need for a more comprehensive tool was only made more visible [AriOS]. The Freiburg team, RoboCup was created as an international research and ed­ amongst others, has developed its own viewer, releasing it to ucation initiative, aiming to foster artificial intelligence and the rescue community. Freiburg's 3D viewer [KGOS] is one robotics research, by providing a standard problem, where of the most used by the community, second only to Mori­ a wide range of technologies can be examined and inte­ moto's 2D viewer [Mor02], which is included in the official grated [Ano06], simulator package. The huge success ofthe RoboCupSoccer international re­ search and education initiative, led the RoboCup Federation Our purpose was to develop a visualization tool to the to create the RoboCupRescue project focussing on Urban Robocup Rescue Domain, that features a multi-view over Search and Rescue (USAR) operations [Ano06]. the simulated environment. Camera positions are restricted to existing rescue agents or entities (such as buildings, po­ The RoboCupRescue Simulation League consists of a lice, fire brigades, etc). Aerial views are planned but not yet simulated city in which heterogeneous simulated robots, act­ implemented. Users monitoring the rescue simulation should ing in a dynamic environment, coordinate efforts to save benefit from such tool since they are provided with a fixed people and property. Heterogeneous robots in a multi-robot (and small, e.g. four) number ofviews selected based on cri­ system share a common goal, but have different abilities teria that tries to optimize the relevance ofthe virtually cap­ and specializations, adding further complexity and strategic tured imagery to the understanding of the evolving simula­ options. These systems can manifest self-organization and tion. Our viewer is partially based on [KGOS]. complex behaviors even when the individual strategies ofall the robots are simple. The team-programmed robots are of three different types: Fire Brigades, Police Forces and Am­ 3.1. Problem description bulance Teams. Fire Brigades are responsible for extinguish­ ing fires; Police Forces open up blocked routes; and Ambu­ The problem can be informally stated as: In an urban res­ lance Teams unbury Civilians trapped under debris. Each of cue setting there are m visualization agents that can obtain these types of robots is coordinated by an intelligent cen­ views over the scene. The objective is to find an optimal set tre responsible for communication and strategies. In order to of k views that better describe the simulation for each mo­ obtain a good score, all these robots work together to explore ment. These k views can have a different purpose, but for the city, extinguish fires, and unbury Civilians. the sake of simplicity, we restrict the discussion to the mon­ itoring ofemergency situations. The visualization agents are controllable in the sense that one can affect their viewing parameters. The optimization problem can be fonnalized as follows: v = {VI,'" VOl} v;=!(Pas;,VD"VUP"FoV,) iE {l, ... ,m} Figure 1: RoboCup Rescue Simulated Environment MV = {mvi , .. ,mvd whereMV c V and mv; ¥-mvj Vi¥-J MAXIMIZE: Q(MV) = The simulated environment, Fig. I is composed ofseveral sub-simulator modules such as the fire simulator and block­ L)=I L7=1 Vis(e{).Red(e;IMV)(WI.Rel(e;) + W2. Ecc(e{)) ade simulator. This structure allows for independent module development, permitting the addition of new modules and making the simulator system more realistic. where, E is the set of n entities that have relevance in the scene (buildings, agents, etc). V is the set ofdifferent views (equals the number of agents/entities with viewing capabil­ 3. Visualization of RoboCup Rescue Simulations ities). Each view is characterized by common camera pa­ There are several tools for visualizing RoboCup Rescue rameters, as the position Pas" view direction vb" relative Simulations. Log viewers are used to track the evolution and camera orientation VUP; and field of view FoV,. Aspect ra­ result ofrescue simulations. Some viewers have been written tio is not being considered as it remains unchaged. MV is so far, enabling different viewing perspectives of the simu­ a multiview setting consisting of k distinct views from V. lation, but all of them lack the functionality of a good de­ The problem is to find the optimal MV set, with appropri­ bugging viewer [AriOS]. Some teams have perfonned work ate view parameters, that describes the rescue scenario with o P. M. Moreira, L. P. Reis & A,