A layered architecture for office delivery robots
AGENTS '97 Proceedings of the first international conference on Autonomous agents
An Behavior-based Robotics
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
Propice-Plan: Toward a Unified Framework for Planning and Execution
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Unified Behavior Framework for Reactive Robot Control
Journal of Intelligent and Robotic Systems
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.