Associative Model for Solving the Wall-Following Problem

Abstract

Abstract.A navigation system for a robot is presented in this work. The WallFollowing problem has become a classic problem of Robotics due to robots have to be able to move through a particular stage. This problem is proposed as a classifying task and it is solved using an associative approach. In particular, we used Morphological Associative Memories as classifier. Three testing methods were applied to validate the performance of our proposal: Leave-OneOut, Hold-Out and K-fold Cross-Validation and the average obtained was of 91.57%, overcoming the neural approach.Keywords: Classification, Associative Models, Morphological models, WallFollowing. IntroductionIt is a fact of life that technology makes progress by leaps and bounds, insomuch that robots are more common day by day in our environment. It is possible that in the near future they could be in our homes performing daily homework which is done by us. Nowadays, most of the robots are operated by human beings. Some others can operate under an autonomous hand such as Asimo [1], the Murata Boys [2], the participants of RoboCup league [3], Surena 2 [4] or the HRP-4 [5] among others. We have senses which give us freely movement without colliding against people or obstacles, however, robots need sensors to simulate our senses and a navigation system to be able to move through a particular stage. This navigation system is called Wall-Following which has become a classic problem of the Robotics. Some researchers have proposed solutions using diverse computational tools for improving the performance, these tools are: Genetic programming [6]

Pattern Recognition of mtDNA with Associative Models

“…Due to lower case characters start at the decimal number 97, we subtracted 97 to all the dataset, therefore, the numbers representing the characters are: 0-a, 2-c, 6-g and 19-t. For example, one of the sequences is, gatcacaggt ctatcaccct attaaccact cacgggagct ctccatgcat ttggtatttt then, the corresponding decimal numbers represented as a vector, The vectors to build a min memory are similar but we changed the value of 500 for -500. We used this value because we have observed that the value in the diagonal must be greater than the maximum value of the elements of the input vectors [15]. We want to highlight that we see the recognition problem as a classification task.…”