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Changmin Baek1, Kyunghoon Cho1

  • 1Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea.

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Summary

This study presents a new path planning algorithm for low-cost trajectories that meet mission requirements using Linear Temporal Logic (LTL). The method uses a novel discrete abstraction and deep learning for efficient, superior trajectory generation.

Keywords:
deep learning-based control synthesisformal methodsmission-based path planningsampling-based path planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Theory

Background:

  • Path planning is crucial for autonomous systems.
  • Existing methods struggle with complex environments and mission specifications.
  • Linear Temporal Logic (LTL) offers a formal way to define mission requirements.

Purpose of the Study:

  • Introduce a novel path planning algorithm for low-cost trajectories.
  • Fulfill mission requirements specified in LTL.
  • Enhance sampling-based methods for complex environments.

Main Methods:

  • Developed a multi-layered framework for discrete abstraction without mesh decomposition.
  • Integrated deep learning to model optimal trajectory distributions.
  • Guided sampling processes using discrete abstraction for vertex and target selection.

Main Results:

  • Successfully generated low-cost trajectories meeting LTL mission criteria.
  • Demonstrated effectiveness in complex and high-dimensional environments.
  • Achieved superior performance compared to existing path planning methods in simulations.

Conclusions:

  • The proposed algorithm offers an efficient and effective solution for LTL-based path planning.
  • The discrete abstraction and deep learning integration are key to its success.
  • This method advances the state-of-the-art in autonomous navigation and mission planning.