Programming of the Fire Escaping Paths Using Bayesian Estimated Algorithm

The paper develops a fire monitoring and escaping system that is applied in the intelligent home and uses mobile robots with flame sensor to detect fire sources. The supervised computer receives the locations of the detected fire sources and calculates the risk values of the cross points of the motion platform using joint probability. Then the supervised computer programs motion paths and escaping paths, and controls mobile robots to guide the human leaving the fire field. Mobile robots contain two types moving in the platform. One is the fire detection robot using ultraviolet sensor to search fire sources. The other represents the human walking in the motion platform autonomously. The controller of the two type mobile robot is STCMCS-51 microchip. The fire detection robots receive the motion command from the supervised compute via wireless RF interface. Each mobile robot transmits ID code, position and orientation, positions of detected fire sources and obstacles to the supervised computer via wireless RF interface, too. We classified three steps for fire detection process. The supervised computer controls fire detection robots to search fire sources, uses Gaussian distribution function to present the risk values of the detected fire source, and combines with joint probability to compute the relation risk for multiple fire sources in the first step. In the second step, A searching algorithm and the relation risk values were used as a basis to program the motion paths from the human, and a fire detection robot was assigned to guide the human leaving the fire field. The supervised computer re-programs the escaping routs to avoid detected fire sources and obstacles for the fire detection robots in the third step and controls mobile robots to present the movement scenario step by step.