Optimization of Robotic Mobile Agent Navigation
Sándor Tihamér Brassai1,2, Barna Iantovics2, Călin Enăchescu2
1 Petru Maior University
1 Nicolae Iorga Street, Târgu Mureş
2 Sapientia University
şos. Sighişoarei 1.C., Corunca/Târgu Mureş, Romania
Abstract: The traveling salesman problem (TSP) has many applications in economy, transport logic  etc. It also has a wide range of applicability in the mobile robot path planning optimization . The paper presents research result of solving the path planning subproblem of the navigation of an intelligent autonomous mobile robotic agent. Collecting objects by a mobile robotic agent is the final problem that is intended to be solved. For the robotic mobile agent’s path planning is used an unsupervised neural network that can find a closely optimal path between two points in the agent’s working area. We have considered a modification of the criteria function of the winner neuron selection. Simulation results are discussed at the end of the paper. The next future development is the hardware implementation of the self-organizing map with real time functioning.
Keywords: Robotic mobile agent; neural network; unsupervised learning; computational intelligence.
CITE THIS PAPER AS:
Sándor Tihamér BRASSAI, Barna IANTOVICS, Călin ENĂCHESCU, Optimization of Robotic Mobile Agent Navigation, Studies in Informatics and Control, ISSN 1220-1766, vol. 21 (4), pp. 403-412, 2012.
Robotic mobile agents have a wide range of applications in different areas , such as: access dangerous areas to humans, underwater explorations, monitoring the environment, painting and de-painting applications . In our research, as mobile agent a robotic mobile agent is considered. The main properties of the mobile agent are: the intelligence in operation, autonomy, reactivity and mobility. Many scientists are working on finding new solutions for different subsections of robotic mobile agent and multi-agent  applications such as  navigation, localization, optimal path planning, path following  object detection, movement and modelling  of the mobile robots with multiple implementation solutions . In this paper we will focus on path planning optimization of the mobile agent using neural networks.
We will give solutions for:
- a TSP and a modified TSP problem solving when the agent does not have to get back to the starting point.
- Finding a closely optimal path from the resolved TSP. For solving the TSP a Kohonen map was used with a proposed cost function in the winner neuron’s selection. In the following sections, the TSP problem, the network structure and training, and results with the resolved TSP with Kohonen map and optimization of path planning between a starting node and a target node on the map will be presented.
The paper is organized as follows: Section 2 discuss the problem that we intend to solve and presents some existent solutions for TSP solving; in Section 3 our proposal for path finding is presented with preliminaries of self-organizing map architecture, a modified solution for TSP solving, and the optimal path finding; Section 4 presents the conclusions of the research and the future research direction.
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