Bringing reality to evolution of modular robots: bio-inspired techniques for building a simulation environment in the SYMBRION project Martin Saska and Vojtechˇ Vonasek´ and Miroslav Kulich and Daniel Fiserˇ and Toma´sˇ Krajn´ık and Libor Preuˇ cilˇ Abstract— Two neural network (NN) based techniques for building a simulation environment, which is employed for evolution of modular robots in the Symbrion project, are presented in this paper. The methods are able to build models of real world with variable accuracy and amount of stored information depending upon the performed tasks and evolu- tionary processes. In this paper, the presented algorithms are verified via experiments in scenarios designed to demonstrate the process of autonomous creation of complex artificial organ- Fig. 1. A modular robot overcoming a pit between two ramps. isms. Performance of the methods is compared in experiments with real data and their employability in modular robotics is discussed. Beside these, the entire process of environment data acquisition and pre-processing during the real evolutionary cess of simulation environment building. These required fea- experiments in the Symbrion project will be briefly described. tures will be verified and discussed in the experimental part Finally, a sketch of integration of gained models of environment of this paper. The first requirement is precision of the gained into a simulator, which enables to fasten the evolution of a real complex modular robot, will be introduced. model. Only simulations realised with models matching the reality are meaningful for trials with real robots. Here, one I. INTRODUCTION should mention that important aspects of the model precision are not limited to shape and size of objects in the robots’ A valuable representation of environment is an important workspace only but also additional properties important for part of modular systems aiming to employ evolutionary the simulation of movement and friction of robots on objects’ principles for creating a complex robot from simple au- surfaces need to be considered. Another important aspect tonomous modules. One can find three main aspects where is the possibility to set the resolution of gained models such a model of the workspace would be crucial in modular which may affect the precision. For an initial growing of robotics. First of all, the environment itself affects the the modular organisms via the evolutionary process a spare purpose of the evolution. In most of the modular systems model with lower amount of information could be sufficient, [1], robots are able to form complex structures to be able which speed-up the evolution. Contrariwise, an evolution to overcome obstacles in the operating environment which of robot’s behaviour to properly interact with environment would be impossible for single robots (see Fig. 1 for a requires a more precise model. Therefore, relations between motivation). Secondly, the single robots, but also the complex the complexity of models, their quality and computational organism itself, need a model of the environment to be able efficiency of simulations are discussed in the experimental to aggregate to more complex structures to solve their tasks section of this paper. to navigate to docking stations etc. The third purpose of the Many projects have appeared in last two decades in the environment modelling in modular robotics arises mainly in field of modular robotics. They have addressed problems like the Symbrion project [2] where a concept of an intensive mechanical design, motion planning, self-reconfiguration and evolution via simulations has been proposed and investigated. morphology evolution of multi robot organisms [1]. Beside It was shown that it is inefficient and possibly dangerous (for the construction of mechanical robots, software simulators the robots) with existing technologies to leave robots to do have been often developed. They can be used prior to hard- autonomous reconfigurability blindly. Rather a combination ware experiments, e.g., for evolving a 3D morphology of an of numerous runs of evolutionary algorithms in simulators organism. Several projects developed their own simulators, and only a few hardware trials of perspective structures found like Molecubes [3] or Symbrion/Replicator [4], other use in simulated environment is preferred. commercial simulators, e.g., Webots [5]. The simulators Such a close connection of simulations and a real evolution usually provide a simple arena with none or a small number of robotic organisms brings additional demands on the pro- of elementary obstacles. In Symbrion project, the robots are Martin Saska, Vojtechˇ Vonasek,´ Miroslav Kulich, Daniel Fiser,ˇ Toma´sˇ designed to survive in more complicated environments with Krajn´ık and Libor Preuˇ cilˇ are with Department of Cybernetics, Fac- holes and more complex obstacles. To be able to verify ulty of Electrical Engineering, Czech Technical University in Prague. the results of simulations with real robots, the physical fsaska,vonasek,kulich,tkrajnik,[email protected], danfis@danfis.cz This work is supported by European Union under the grant Symbrion- properties of robots and arena in the simulator have to be Enlarged no. 216342. and by MSMTˇ under the grant COLOS no. LH11053. close to parameters of real robots and environment. For more complex environments it is important to have an automatic tool for creating the simulation environment. In this study, we describe how 3D reconstruction techniques can be used for this task. Methods for surface reconstruction can be roughly divided into two categories [6]. Static methods, such as [7], [8], [9], [10], are based on geometric techniques. As they connect a set of input points, the size of the resulting mesh corre- sponds to the number of the input points. Post-processing is thus needed to get results with desired resolution. Learning Fig. 2. The mobile robot equipped with two SICK laser range finders methods and self-organizing structures as their special case scanning the environment. belong to dynamic methods [11], [12]. These approaches approximate the input point cloud by adaptation of an initial mesh [13] by modification of geometry, size or/and topology II. APPROACHES FOR BUILDING A SIMULATION of the initial mesh. ENVIRONMENT A. Data collection and pre-processing One of the targets of the Replicator/Symbrion projects The first step of the effort of bringing reality to the is a long-term robot autonomy in a changing world. Since evolution of modular robots lies in the data collecting. We the environment model must keep up with the current state have utilized a differential drive G2Bot robotic platform of reality, a mechanism for model refinement and update [14]. The robot has been equipped with two perpendicularly must be considered. Using bio-inspired dynamic methods for mounted SICK laser range finders (see Fig. 2 for details on surface reconstruction, the proposed model can be updated the measurement configuration). While the first horizontal with sensory data acquired by the robots, which operate laser together with odometry is employed for the robot within the modeled environment. The robots can not only localization, the second vertical laser pointed to the robot provide new range measurements, which contribute to pre- side is employed for the 3D environment scanning. Although cision and completeness of the generated meshes, but also the robot is localized using the on-board sensors, we plan might add information which cannot be perceived by range to use the Ubisense localization system [15] for the final sensors at all. For example a robot, which is equipped by experiments. a camera, can add texture or color information to the mesh During the data collection, the robot has to be driven in or recognize, localize and add known objects (e.g., power the environment in such a way that the vertically mounted plugs) to the map. Taking into account all aforementioned sensor scans obstacles completely. Since the coverage path requirements, we have selected two appropriate methods of planning is not the aim of this paper, we have teleoperated 3D modelling which will be adjusted and verified for the the robot through the environment during experiments but purpose of modular robotics here. one can find plenty of algorithms for autonomous control of robots [16]. In the presented approach, the collected data (see Fig. 3(a) for example) has been stored and processed This paper is structured as follows. The core of the off-line. Again, the method could be extended for on-line paper is section II describing utilized approaches of en- 3D object reconstruction and environment mapping using a vironment scanning and modelling. Particularly, a robotic state-of-the-art method, but this would exceed the scope of platform adjusted for 3D scanning of workspace of modular this paper. robots and a procedure of data collection, followed by a Several different kind of errors and uncertainties have specification of methods of post-processing for data errors been identified in the obtained datasets, which requires a and noise reduction, is described in subsection II-A. The pre-processing of data before utilization of methods for 3D algorithms of 3D object reconstruction are described in detail object reconstruction. First of all, shaking of the robot during in subsection II-B with focus on possibility of setting of the scanning need to be compensated. Here, we have taken different resolution of gained models which is important due advantage of the 180 degree view of the SICK sensor which to the required computational time of the simulation. The ensures that the straight floor of the scanned area is always usage of obtained models in physical simulations of evolution included in each scan. Based on the declination of the line- of modular robots and approaches solving interactions of segment representing the floor in the scan, the data are the model of environment with the modular organisms are transformed to correct the unwanted deviation of sensor described in subsection II-C.1.