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In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuse...

Mapping is one of important issue in robotics. In this paper a new vision based odometery free algorithm for representing environment map is proposed. Our contribution is providing a model based on two important types of brain cells. Place cells in hippocampus play main role in mapping and entorhinal cortex layer cells reacting to image features provide main portion of positional cells input. Our contribution is modelling these two important types of cells by Kohonene self-organizing map. Considering Gabor filter compatibility in determining image texture, V1 visual layer is modelled by a Gabor filter histogram and the output of filter on input images is feeding to a Kohonene self-organizing map that models entorhinal cortex layer cells. The model is implemented on real robot equipped with an omni-directional camera in a real 10∗30 meter environment. The results showed that the robot can make an appropriate map and determine its location in drawn map.

In this paper, a novel method for mobile robot map representation is presented. We introduce a manifold based map which is constructed by mapping the color histogram of omni-directional camera images into a low dimensional space while preserving local and global geometry. The geometry is preserved using orthogonal and independent feature vectors consisting of the Euclidean data difference and the nearest and farthest neighbor data vectors. The introduced dimension reduction method has shown superior performance compared to LLE and PCA in benchmark data sets providing a hard dimension reduction of 768 into 3. This mapping matrix has been used for real time robot localization and mapping in real world experiments. In large environments, adjacent places share the same set of objects and features therefore dimension reduction may end up in a non-smooth manifold. To apply the method in large environments, first a K-Means method is used to cluster the environment and then each obtained cluster is processed separately. Successful experiments on 7 different data sets show effectiveness of this method for robotic applications such as mapping, visual place classification and kidnaped-robot localization.

In this paper, generalized intelligent water drops (IWD) algorithm is proposed to solve robot path planning problem. The proposed algorithm has two levels; first level, finds best global path. Second level, performs local search at relatively near distances of global path and reduces its length and response time. IWD algorithm, like other nature-inspired algorithms, has not a mechanism to deal with constrained optimization problem. So, a mechanism has been proposed based on repair of infeasible solutions. In this mechanism, proposed local search operator repairs some of infeasible solutions. Simulation result shows ability of IWD algorithm in finding the optimal path.

the most important factor for a mobile robot is navigation. Success in localization is one of the four requirements of navigation which includes perception, localization, cognition and motion control. Localization is usually done in two ways: global localization problem and position tracking problem. In the global localization problem the robot does not know its initial position and it has to localize itself. In the position tracking problem for a robot given its initial position, the goal is to keep track of the position while the robot is navigating through the environment. In this paper we propose a method using an evolutionary algorithm for mobile robots' global localization. We use Imperialist Competitive Algorithm (ICA) to achieve our goal. The method that is used for global localization works in two steps: First we estimate X and Y and then we find the best direction for X and Y which we estimated in previous step. The results which have been demonstrated in the experimental results section show that our algorithm works very accurate in all of the positions that we located the robot for our tests with acceptable number of iterations.