Orchestrating Method Ensembles to Adapt to Resource Requirements and Constraints During Robotic Task Execution

Robot behavior designers commonly select one method - e.g. A* or RRT - that is assumed to have the appropriate trade-off for a given domain between computational load, computation time, and the quality of the result of the method. We propose ensemble orchestration patterns , which evaluate multiple...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 10; H. 2; S. 1186 - 1193
Hauptverfasser: Lay, Florian S., Domel, Andreas, Lii, Neal Y., Stulp, Freek
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
behavior based systems
Computational efficiency
Constraints
Ensemble learning
Extraterrestrial measurements
Measurement
methods and tools for robot system design
Motion planning
Navigation
Path planning
Planning
Robot sensing systems
Robotics
Robots
Service robots
Space robotics and automation
Tradeoffs
ISSN:2377-3766, 2377-3766
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Zusammenfassung:Robot behavior designers commonly select one method - e.g. A* or RRT - that is assumed to have the appropriate trade-off for a given domain between computational load, computation time, and the quality of the result of the method. We propose ensemble orchestration patterns , which evaluate multiple methods, and select the best result, thus exploiting the complementary advantages that alternative methods often have. By implementing different termination, preemption, constraint enforcement and selection schemes, different patterns lead to different (predictable) resource trade-offs. Thus, rather than selecting and committing to only one method, a designer chooses the appropriate pattern and constraints for the desired trade-off, and the pattern then realizes the selection on-line. We apply these patterns to various subtasks that are prevalent in our Surface Avatar ISS Technology Demonstration Mission, such as navigation, motion planning, and registration aswell as to a subtask in the service robotics domain in a simulated experiment. In our evaluation, we demonstrate that these patterns can effectively exploit increased resource budgets or relaxed constraints to find better solutions, and adapt the selection to different situations.
Bibliographie:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3518077