Selfmodeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network * In this paper, a selfmodeling method based on a causal network is proposed for the tracking control of the Crawler Fire Fighting Robot (CFFR). The method mainly consists of two parts, one is a motion model, based on data driving, learning to establish the correspondence between control signal sequence and vehicle motion, estimating the motion state of the next moment from historical data, eliminating complex CFFR modeling. The other is the tracking network. Based on the simulation data of the motion model, the relationship between the target trajectory and the current control command is learned, which simplifies the design and cumbersome tuning of the complex controller. The effectiveness of the proposed method is verified in both simulated and realworld environments. Qualitative and quantitative experimental results verify the accuracy of the tracking.

A Target Position Decision System based on Eyewitness Account for Exploring Disaster Environment The main contribution of the article lies in the realization of the target position decision system for exploring a disaster environment based on eyewitness accounts. This is achieved using the modeling of Bayesian networks to infer locations of interest from eyewitness accounts that are divided into words and the semantic map representation based on image processing. Recently, an exploration of a disaster environment by employing rescue robots before firefighters enter into the dangerous environment has increased in application. This is because getting information in advance, such as a victimu0027s position or a change in environment can protect firefighters in hazardous conditions resulting from smoke, fire or pollutants. However, the gap of information between the symbolic meaning, the information that humans understand, in an eyewitness account and the target coordinates on the map makes it difficult to apply the system for exploring a disaster environment based on robots into the real situation. The proposed system automatically infers the room type of interest using the Bayesian network that is designed based on survey of 70 firefighters and matches it to coordinates from the semantic map. Then, the system distributes the target positions to the robots in order to explore the disaster environment. To verify the performance of the proposed system, we conduct the system verification based on one scenario to make a decision of a target coordinate on the map.