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CNN-Based Genetic Algorithm International Journal of Computational Intelligence Systems, Vol.2, No. 2 (June, 2009), 124-131 Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot Alireza Fasih Transportation Informatics Group, Institute of Smart Systems Technologies, University of Klagenfurt Klagenfurt, Austria E-mail: [email protected] Jean Chamberlain Chedjou Transportation Informatics Group, Institute of Smart Systems Technologies, University of Klagenfurt E-mail: [email protected] Kyandoghere Kyamakya Transportation Informatics Group, Institute of Smart Systems Technologies, University of Klagenfurt E-mail: [email protected] Abstract A new learning algorithm for advanced robot locomotion is presented in this paper. This method involves both Cellular Neural Networks (CNN) technology and an evolutionary process based on genetic algorithm (GA) for a learning process. Learning is formulated as an optimization problem. CNN Templates are derived by GA after an optimization process. Through these templates the CNN computation platform generates a specific wave leading to the best motion of a walker robot. It is demonstrated that due to the new method presented in this paper an irregular and even a disjointed walker robot can successfully move with the highest performance. Keywords: Cellular Neural Networks, Robot locomotion, Simulation, Genetic Algorithms. 1. Introduction of animal walking motion with the aim of optimizing the energy consumption [2-4]. It is well-known that the Nowadays, some of the main goals of robotics science, walking motion of animals is of a stereotype. In a large mechatronics and artificial intelligence lie in designing variety of animals a central neural controller does mechanisms close to or mimicking as good as possible organize/coordinate the motion. A central neural some natural structures or animal behavioral models. controller (e.g. the central pattern generator (CPG)) is a According to this theory, the nature selects the powerful main unit for controlling limbs for walking [5]. The and stable genes for breed, and weak genes CPG unit does contain all the mechanisms needed to fall/disappear in the nature [1]. The good genes that can generate the rhythmic pattern of movement. This unit is adapt the animal structure to the environment have suitable for designing walker, swimmer, or flyer robots higher chances for breed and evolution. The animal which exhibit motion close to natural locomotion locomotion is trained and adapted according to the mechanisms. Due to recent advances in electronics and animal’s body structure. One key issue in the training the ability of cellular neural networks to solve partial process is based on the energy saving. This justifies the differential equations in real time, it is possible to striking interest devoted to the modeling and simulation simulate a Reaction-Diffusion model by a specific CNN Published by Atlantis Press Copyright: the authors 124 A. Fasih et al. architecture, the so-called reaction-diffusion cellular & = - + B ∗ + A ∗ + IyTuTxx (1) neural network (RD-CNN). A striking interest has been devoted to the robot where, ‘T ’ denotes the 3×3 feedback template and ‘T ’ control based on the RD-CNN technique [1, 5, 6]. In A B stands for the 3×3 control template. ‘I’ is a bias value this technique, the mathematical model describing the and ‘y’ is the nonlinear output sigmoid function of each robot behavior must be well-defined. This is a serious cell. ‘u’ denotes the input value and ‘x’ is the state of limitation as modeling the complex behavior of robots is each cell. The input value is discretized into pixels and challenging. In this paper we introduce a robots control is represented in a table of numbers called matrix. The method which is not based on the mathematical size of this matrix depends upon the number of joints in modeling of the robots behavior. It is rather a general the walker robot. In Eq. 1, the stars stand for and effective method combining CNN with genetic convolution operations. algorithms (GA). This method can support and drive The genetic algorithm (GA) is a heuristic search many types of structured and unstructured walker technique used in computing to find either exact or robots. The method/approach is based on both the approximate solutions for optimizing a given problem. natural modeling and the use of computational units The GA is an evolutionary algorithm that uses close to biological models. A combination of both CNN techniques inspired from biology such as inheritance, (i.e. for computation) and GA (i.e. for optimizing the mutation, selection, and crossover. In this paper, this nature) is a good tool for modeling and controlling algorithm is used for finding the best templates for robots dynamics. The central parts of this scheme are optimum robots locomotion. made-up of a CNN processor and an evolutionary The complete structure of the system used for the training unit. A Cellular neural network (CNN) is a training process is shown in Fig. 1. This structure parallel computing paradigm similar to the artificial consists of six main parts: (1) Initial Population; (2) neural networks computation platform, with the Crossover; (3) Mutation; (4) Fitness Function; (5) difference that in CNN the communication is allowed Decoding; (6) Cellular Neural Network Simulator. In between neighboring units. This feature of the CNN Fig. 2 the connections between the robot processor makes it a good computation platform to actuators/hinges and the CNN outputs are shown. These analyze the dynamics of biological neurons. This paper connections are exploited in the control of both robot shows the possibility of directly driving a walker robot hinges and actuators. Wave rhythms are generated from by an evolutionary training of a CNN processor. This the CNN processor outputs which can drive the walker method is further efficient to model widespread natural robot on a specific path and/or direction depending on locomotion mechanisms of animals (e.g. worms, insects, the high level task each of which consists of many low quadrupeds, biped, etc) [7]. This locomotion is modeled level tasks. After the learning phase, the output waves in the 3D space describing the real environment and in can drive the robot with a minimum energy and a good very difficult situations (i.e. rough, bumpy, and/or scaly efficiency. This driving depends upon specific choices surfaces) as well. The challenging focus is finding the of templates values. Each template set is a solution for best signal for driving walker robot joints with minimum energy consumption and the best locomotion performance. This can be achieved by finding suitable CNN templates to generate an efficient wave for driving the walker robot joints. This paper is organized as Best Gene Init Population follows. Section 2 discusses the use of genetic Template algorithms for optimizing the CNN templates. Section CNN 3 presents the training algorithm and some simulation Crossover results as well. Section 4 formulates some concluding Out Add remarks. Further, the quintessence of the results Mutation obtained is summarized, and some open research Fitness questions are outlined. Decoding Sort 2. Using Genetic Algorithms for CNN template optimization Worst Gene The concept of Cellular Neural Networks (CNN) was Fig. 1. System Architecture Diagram introduced by Leon O. Chua and Yang [8]. CNN is a computation platform which is mathematically modeled by Eq. 1 Published by Atlantis Press Copyright: the authors 125 CNN-Based Robot Controller driving the robot by means of (or by performing) some specific low level tasks. For optimizing these solutions, the templates must be coded as chromosomes as shown in Fig. 3. In the initialization phase, Fig. 3 generates many random chromosomes (in this case CNN templates), each being a solution for driving the robot. In fact, each chromosome is a CNN template that is reshaped in a one dimensional array. According to Fig. 3, each chromosome does contain a feedback template, a control template and a bias value. Various methods exist (in genetic algorithms) for coding data as chromosomes. This paper implements two different methods for coding and generating chromosomes. The first method is based on the IEEE-754 scheme which is a floating point technique. In this technique, each value must be converted to binary format according to the IEEE-754 floating point technique. The IEEE floating Fig. 2. Robot hinges connection to CNN point format consists of three main parts: the sign, the exponent, and the mantissa [12]. The number of bits for each field is shown in the table below. Table 1: Single Precision - IEEE Floating Point Format Structure Sign Exponent Mantissa 1 bit 8 bit 23 bit With the floating data types mentioned in Table 1, it is possible to store values between the ranges[ × −45 ×104.3,105.1 38 ]. The use of this method as a gene coder requires the definition of a mask for Fig. 3. Template Encoding in an Array List some bits. Otherwise, the random chromosome generator will generate values out of the range[+− 5,5 VV ]. This condition is of high We need the robot to escape from a position without any importance as a hardware implementation (using TTL specific direction. For this purpose, we must define a devices) of this algorithm is under consideration. In the simple function for measuring the length between the second method implemented, a “real” data type value is central point (i.e. the gravity center) of the robot and the used as a chromosome coding. For this step, a random initial position. In this example, we didn’t define any function generates a value in the acceptable range. The detail for the locomotion behavior. After generating new implementation of this method is easier than of the first chromosomes, we obtain the corresponding fitness method. The results from the two methods are compared value by applying the fitness function.
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