A behaviour-based blackboard architecture for reactive and efficient task execution of an autonomous robot

Mobile robot control architectures have matured in two major directions: hierarchical and behaviour-based. Behaviour-based architectures enable rapid response to environment changes and they feature robustness in real worlds. Earlier behaviour-based systems have suffered from difficulties in system modularity, state representation and integration of world models, In this paper we present a behaviour-based mobile robot system for task execution. The behaviour model of this system consists of a number of motion behaviours, including reflexes and voluntary motion behaviours, and knowledge acquisition modules providing supporting information. Execution of a task is regarded as a problem-solving process. A blackboard model is introduced to overcome some shortcomings of the behaviour-based architectures, especially concerning modularity and task execution capability. The concept of attention is introduced in the behaviour control, which is more advantageous than the behaviour control mechanisms presented in the literature. Its introduction results in situation-dependent behaviour coordination. For efficient task execution, environment knowledge is maintained in the memory. Task-achieving behaviour is designed to make use of the memory when available.