Optimal Path Forecasting of an Autonomous Mobile Robot Agent Using Breadth First Search Algorithm

Abstract — Predicting and identifying an optimal navigational forecasting for a mobile robot agent is considered to be one of the key challenges for researchers in the field of AI and robotics. A mobile robot agent participating for wide range of mission critical application will explore into an unknown environment will be challenging criteria, considering its contraints such as time, cost, energy, exploration distance etc.. When the autonomous mobile robots agent decides its action, it is necessary to plan its movement more precisely and optimally. This paper presents the use of search algorithm in optimal path forecasting with its navigation controlling mechanism of a autonomous mobile robot agent navigational systems. The autonomous mobile robot agents are modelled to work independently through its intelligence without any human intervention. This paper aims on studying the movement details of an autonomous mobile robot agent participating in dynamically changing environment using Breadth First Search (BFS) algorithm. The system evaluation is validated using Graphical User Interfcae (GUI) based test bed for robots called Robosim and the efficiceny of the system is measured via simulation resutls through a defined complex arena. Simulation results proven that the applying the BFS algorithm in a unknown envirnoment explores much faster then heuristic based other path palnning algorithm