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This study introduces a new combined task and motion planning (CTAMP) system for robots. The system effectively plans robot actions and motion, even with environmental changes and control errors.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Planning and Reasoning

Background:

  • Robotic technologies require sophisticated planning for complex tasks.
  • Combined Task and Motion Planning (CTAMP) integrates symbolic and geometric reasoning.
  • Existing CTAMP systems often face challenges due to the fundamental differences between symbolic and geometric reasoning.

Purpose of the Study:

  • To propose a novel CTAMP system that effectively bridges symbolic task planning and geometric motion planning.
  • To develop an interface module that facilitates seamless integration between task and motion planning.
  • To enhance robot adaptability and robustness in dynamic environments.

Main Methods:

  • A CTAMP system generating symbolic action sequences via task planning.
  • Geometric verification of each action using off-the-shelf motion planners.
  • An interface module defining action models in PDDL (Planning Domain Definition Language) within an action library.
  • Automatic provision of necessary information to planners by the interface module.

Main Results:

  • Successful implementation in three simulated manipulation task experiments.
  • Demonstrated effectiveness in responding to environmental changes.
  • Showcased robustness against uncertainties, including recognition errors and robot motion control inaccuracies.

Conclusions:

  • The proposed CTAMP method is effective for complex robotic manipulation.
  • The system enhances robot adaptability and reliability in uncertain conditions.
  • The integration approach simplifies CTAMP by leveraging existing planners and reasoners.