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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Path Planning

Background:

  • Efficient coverage path planning is critical for robots in cleaning, painting, and mining.
  • Existing methods struggle in complex environments due to fixed robot morphologies.
  • Novelty lies in addressing these limitations with adaptive robot design.

Purpose of the Study:

  • To develop an efficient coverage path planning algorithm for a self-reconfigurable robot.
  • To enhance area coverage performance in complex and narrow spaces.
  • To validate the proposed approach in simulation and real-world scenarios.

Main Methods:

  • An A-star based zigzag global planner was designed for a Tetris-inspired reconfigurable robot (hTetro).
  • The algorithm generates waypoints considering robot morphology adaptation.
  • Validation was performed using the Robot Operating System (ROS) in simulation and real-time experiments.

Main Results:

  • The proposed planner successfully generated adaptive waypoints for narrow spaces.
  • Superior area coverage performance was demonstrated compared to traditional methods.
  • Experimental results confirmed the efficiency in both simulated and controlled real-time scenarios.

Conclusions:

  • The A-star based zigzag planner effectively improves coverage path planning for reconfigurable robots.
  • Adaptive morphology significantly enhances robot performance in complex environments.
  • This approach offers a promising solution for autonomous cleaning and similar applications.