Autonomous Mobile Robot Task Selection and Configuration Using Constraints

An autonomous mobile robot must be capable of rationally selecting its tasks in order to achieve some state or goal. Rule-based systems encode system knowledge into a set of rules that guide the robot. The dynamic nature of the world is seldom consistent enough to support the rigidity of rules. A new decision maker is needed that expresses the problem as intuitively as rule-based systems with flexibility, extensibility and generalizability. In this paper, a constraint-based framework and decision maker for robot task selection and configuration is presented. Tasks under this framework are modeled as constraint satisfaction problems. The new decision framework is capable of guiding a variety of mobile robots through rational decisions for task selection and configuration. The framework is implemented and demonstrated on an autonomous mobile robot for polar research. This work demonstrates that constraint-based decision making is a viable approach to robot task selection and configuration, and performs better than rule-based systems over a variety of applications.