SENSOR FUSION FOR MINING ROBOTS

The use of robots for work in hazardous or unpleasant environments is one factor driving the demand for machines of ever-increasing autonomy and intelligence. Such machines are required to sense and interpret situations, plan strategies, and execute tasks with nearly absolute reliability. Negotiation of complex environments requires the use of a variety of different sensor types and the interpretation of conflicting or missing data, diagnosis of faulty sensors, and the ability to reconfigure a system to work with a partially inoperative sensor suite. This paper focuses on the issues of integration of information from disparate sensor types in the presence of noise and uncertainty. The application is a mobile robot called the autonomous navigation testbed being used at West Virginia University for research in mining robot applications. This paper describes both traditional control techniques and neural-network-based methods being used to interpret data from a variety of sensors on the mobile testbed.