Keywords: Humanitarian demining, mobile robot simulation, operator training. I. Introduction

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1 Mobile robot virtual training system for robot operators J. Będkowski, M. Pełka, P. Musialik, A. Masłowski Institute of Mathematical Machines, Ludwika Krzywickiego 34, Warsaw In this paper the progress of development of multi robot training system, dedicated for use in TIRAMISU project, is described. The system allows for training of multiple UGV (Unmanned Ground Vehicles) operators during one training scenario. Maximum number of user is 10. The core of the system are VORTEX physics engine, for robust simulation, and OSG (Open Scene Graph) for rendering purpose. The system allows for online, robust simulation of the robots and environment. System architecture allows for scalability: new user can be attached dynamically. The robots can interact with each other, including colliding and realistic gripping simulation. Two models of robots are available in the simulator: a) irobot-packbot b) LOCOSTRA - a TIRAMISU dedicated robot in two configurations: equipped with a UXO (UneXploded Ordnance) detector or a trailer for UXO transportation. An example of multi robot mission will be shown. Keywords: Humanitarian demining, mobile robot simulation, operator training I. Introduction This paper repost of the progress of development of high fidelity simulator for TIRAMISU project. Currently a high number of multi-robot simulators exist on the market. Examples are: Gazeboo[1], USARsim[2], ARGoS[3], SARG[4]. A comparison of different physic simulation systems can be found in [5]. However our experience form other projects thought as that often such ready to use environments lack in robustness for serious gaming simulation purpose ( example [6]). Our experience is related with joint simulation and gasping in Nvidia PhysX[7] engine. If such a limitation is reached it is often very hard to improve existing software in the middle of simulator development, especially in time critical projects. This leads to subpar decisions about lowering accuracy or using bypass methods(unrealistic parameter tuning). Thus for current work we decided to develop current simulator based on a profesional serious games engine VORTEX [8]. VORTEX assures high Rigid Body simulation fidelity. The accuracy assures proper training experience for a sensitive task of demining and UXO removal. For visualization purpose OSG (Open Scene Graph) is used. The communication is done using TCP and UDP protocols. The report is organized as follows. Section 2 describes the architecture of the software along with the description of features and functionalities. Section 3 describes example mission scenario done using the simulator. The paper finalizes with conclusions and future work in chapter 4. II. Architecture The simulator is designed to allow dynamic changes to the environment. The core element is the main simulation server that is responsible for physics calculation. This is the only part of the system that performs direct VORTEX computation. The system is designed to work

2 best with use of multiple computers. Central computer runs the server, where users each have it s own machine. This allows for performance to be equal for all users, independent of their computers configurations. Users connect to the system as clients. During initialization the server sends the location of all scene objects to the clients. Initialization of communication and initial scene configuration are send via TCP connection. During exercise execution only information about objects that had their state changed are send. To lower network traffic updates are send via UDP using producer-consumer communication. Each user has access to camera and sensor view of one robot, along with controls. Control orders to each robot are send by TCP. The overview of the architecture is shown in figure 1. Figure 1. System architecture. The main server module can be used as Trainer overview. From here the Trainer has access to a global view of the scene and views from each robot camera. Additionally the trainer can customize the scene configuration. All objects of the scene, along with parameters, can be customized. Robots configuration can be changed, by, for example, removing cameras, simulating damage. All configuration is done using XML files. Additionally the trainer can change lighting and weather conditions. The system can work fluently with up to 10 users being connected simultaneously. This number may increased by providing better network connection. Simulated robots may interact

3 with each other. The interaction includes: proper collision model, realistic joint model that allows to simulated a robot with a trailer movement, realistic friction model that allows for one robot to be transported by another and proper grasping model that uses friction for simulation instead of false joint creation. III. Example mission scenario Figure 2. Three robots used in the example mission Figure 2 shows the robot models used during the scenario. The goal is to locate and remove an UXO. Three robot interact in this scenario. First is LOCOSTRA equipped with GPRGround Penetrating Radar. The operator is responsible for exploring a ruble pile and locating the dangerous material. In second stage, a LOCOSTRA, equipped with a trailer arrives at the scene. The trailer has a hazard disposal bin. Along with the second robot arrives the third, PackBot, which is brought on the trailer. PackBot operator is responsible for leaving the trailer by a ramp, picking the UXO and putting it to the bin and then getting onto the trailer. This tasks has to be done only using the cameras of the PackBot robot. Figures 3,4,5 and 6 show different stages of the mission: UXO localization, leaving the trailer, putting the UXO into the bin and getting onto the trailer. Picture 7 shows the view from one of the PackBot s cameras.

4 Figure 3. Mission execution: UXO search and removal Figure 4. View from PackBot camera.

5 For this experiment the server runed on a modern mobile computer equipped with Intel i7 CPU and mid-end graphics cards. We did not observe any performance issues. The frame rate was above 25 fps for the Trainer console, higher on the clients. This proves that the systems is robust enough for this type of scenarios. IV. Conclusions and future work This paper presents a short description of progress in development of training simulator for TIRAMISU project. The system allows for multi robot simulation and concurrent task execution. Using VORTEX physics engine as core allows for high fidelity modeling of robot s interaction including: collision, grasping, towing and friction. Client server nature of the system allows for maximizing cost to performance ratio and producer-consumer UDP synchronization lowers the communication load and assures proper simulation speed.the system allows for dynamic client addition. Future work on the system will concentrate on implementing events for documenting object interaction and integrating digging capabilities with the scene module. Alongside the certification module is being developed. Acknowledgments: This paper has received funding from the European Union s Seven Framework Programme for research, technological development and demonstration under grant agreement no Bibliography: [1] N Koenig and A Howard, Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator, a paper presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004 [2] S. Carpin, M. Lewis, Jijun Wang, S. Balakirsky, C. Scrapper USARSim: a robot simulator for research and education Robotics and Automation, 2007 IEEE International Conference on In Robotics and Automation, 2007 IEEE International Conference on (2007), pp , doi: /robot [3] C. Pinciroli, V. Trianni, R. O Grady, G. Pini, A. Brutschy, M. Bram-billa, N. Mathews, E. Ferrante, G. A. Di Caro, F. Ducatelle, T. Stirling, A. Gutierrez, L. Gambardella, and M. Dorigo. ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. In Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), [4] J. Craighead, Distributed, game-based, intelligent tutoring systems - the next step in computer based training?, in Proc. CTS, IEEE 2008, [5]A. Boeing and T. Braunl, Evaluation of real-time physics simulation systems, in Proc. 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, GRAPHITE 07, ACM 2007, [6] Evan Drumwright, John Hsu, Nathan Koenig, Dylan Shell, Extending Open Dynamics Engine for Robotics Simulation, Simulation, Modeling, and Programming for Autonomous Robots Lecture Notes in Computer Science Volume 6472, 2010, pp [7] Nvidia PhysX, - March 2014 [8] VORTEX, - March 2014