This article describes a new approach for control systems for an autonomous mobile robot by using sandwiches of two different types of neural network. One is a neural network with competition and cooperation, and is used for recognizing sensor information where synaptic couplings are fixed. The second is a neural network with adaptive synaptic couplings corresponding to a genotype in a creature, and used for self-learning for the wheel controls. In a computer simulation model, we were successful in obtaining four types of robot with good performance when going along a wall. The model also showed robustness in a real environment.
Fish schools behave like a single organism, and this offers considerable survival advantages. In our simulations, a fish school is well organized, without a leader, and behaves like a single creature depending solely on the interactions among individuals. This kind of system can be said to be typical of "complex systems." In this article, it is shown that fractal evaluation is useful to understand the features of fish school movements. We make clear the validity of fractal analyses to quantify fish school movements through evaluations of simulated fish school movements and sardine movements. These fractal analyses show that we need two different fractal dimensions (D1, 02) to understand the features of fish school movements: D1 corresponds to the smaller coarsening levels, and D2 corresponds to the large coarsening levels. The linear analyses in log-log space give an excellent fit with both the simulated movements and the sardine school movements. In approaching complex systems or complex behaviors, fractal analyses have attracted wide attention in mathematics, physical sciences, and information science. The fractal evaluations here convince us that we are coming close to understanding the structure of complex movements of animals.
This article presents simulation models of autonomous Khepera robots which are assumed to be running on a highway. Each robot acts by following the fish-school algorithm. Although a school of fish does not need a special individual to lead it, an autonomous movement emerges from interactions among neighboring bodies. Our goal is multirobots which behave safely, with no accidents, solely through interactions with their surroundings. When Khepera robots run freely while sensing neighboring robots or the guard rails along the road by means of an infrared ray, the efficiency of their running, such as the distance covered and the number of accidents, is obtained with an evaluation function. Genetic algorithms (GA) with this evaluation function are applied to both the optimization of the discernible region, and the development of driving-type. As a result of optimization of the behavior models of a robot, multirobots could run smoothly while avoiding collisions with other robots or with guard rails, and yet run as fast as possible. The present study of autonomous multirobots approaches the realization of the autonomous control of vehicles running on a highway.
This article describes a new approach to control systems for a mobile robot Khepera by using a neural network with competition and cooperation as the processing unit for the robot sensors. Competition makes only one neuron active, while cooperation keeps them all active. In our research, we find that the Khepera controlled by this neural network can maintain a smoother trajectory than when it is controlled by the output values of its own sensors, especially in noisy environments.
This paper descn'bes a new approach to control systems for an autonomous mobile robot b y using sandwiches of two different kinds of neuml networks. One is a neuml network for recognizing sensor infonnation with a mechanism of competition and cooperation, where synaptic couplings are fixed. The second is a neural network with adaptive synaptic couplings corresponding genotype in the creature and used for self-learning of wheel controls. I n the computer simulative model with both two parts of neural network, we are successful to obtain typical types of robot with a good performance when going along the carved wall. The first part of the networks play a role to make a decision among sensor signals under noisy enuiwnment, while the second part i s effective to adjust the synaptic couplings through genetic operations so that it m a y tmnsfer the outputs from the first stage with the competition-cooperation neuml network (CCNN) to the rotation of robot's wheel. A test is perfonned to show the superiority of CCNN. A robot with C C N N can enter into a narrow entrance with concaved space and strong robustness against several kinds of noises, while the robot without C C N N cannot enter into the space due to the sumunding noise around the entmnce.
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