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This study introduces a novel motion planning strategy for unknown environments, balancing exploration and exploitation to efficiently reach goals. The approach adapts the Canadian traveler problem for optimal pathfinding with evolving environmental knowledge.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Theory

Background:

  • Motion planning in unknown environments requires balancing exploration and exploitation.
  • Achieving generalized reach-avoid specifications is crucial for autonomous agents.

Purpose of the Study:

  • To develop an optimal control policy for motion planning in unknown environments with initially unknown state labels.
  • To fulfill generalized reach-avoid specifications in minimum time.

Main Methods:

  • Representing the environment as a labeled graph with initially unknown labels.
  • Translating the problem to a Canadian traveler problem on an adapted state space.
  • Developing a strategy that exploits a priori knowledge and incrementally reveals the environment online.

Main Results:

  • The proposed strategy enables agents to plan, follow, and replan optimal paths.
  • Edge weights are assigned to balance exploration and exploitation based on current knowledge.
  • The strategy is illustrated on a two-dimensional grid environment.

Conclusions:

  • The developed strategy offers an effective approach to motion planning in unknown environments.
  • It provides a framework for agents to adapt and optimize paths dynamically.
  • This research contributes to advancements in autonomous navigation and decision-making.