Dynamic Behavior Sequencing for Hybrid Robot Architectures

Hybrid robot control architectures separate planning, coordination, and sensing and acting into separate processing layers to provide autonomous robots both deliberative and reactive functionality. This approach results in systems that perform well in goal-oriented and dynamic environments. Often, the interfaces and intents of each functional layer are tightly coupled and hand coded so any system change requires several changes in the other layers. This work presents the dynamic behavior hierarchy generation (DBHG) algorithm, which uses an abstract behavior representation to automatically build a behavior hierarchy for meeting a task goal. The generation of the behavior hierarchy occurs without knowledge of the low-level implementation or the high-level goals the behaviors achieve. The algorithm’s ability to automate the behavior hierarchy generation is demonstrated on a robot task of target search, identification, and extraction. An additional simulated experiment in which deliberation identifies which sensors to use to conserve power shows that no system modification or predefined task structures is required for the DBHG to dynamically build different behavior hierarchies.