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Area of Science:

  • Robotics
  • Computer Science
  • Network Engineering

Background:

  • Traditional wireless sensor networks (WSNs) have fixed spatial features, limiting adaptability.
  • Mobile WSNs offer enhanced robustness and adaptability, making them suitable for target tracking.
  • Existing flocking control models with potential fields for obstacle avoidance can trap nodes in restricted areas.

Purpose of the Study:

  • To introduce a novel cooperative obstacle avoidance model for mobile WSNs.
  • To improve the efficiency and adaptability of mobile WSNs in complex environments.
  • To overcome limitations of traditional flocking control models in obstacle avoidance.

Main Methods:

  • Developed a cooperative obstacle avoidance model based on the traditional flocking control model.
  • Integrated an improved Simulated Annealing (SA) obstacle avoidance algorithm.
  • Defined steering direction using the tangent line of the intersection between node velocity and obstacle edges.
  • Enhanced the model for complex obstacles, including mobile and concave obstacles.

Main Results:

  • The cooperative obstacle avoidance model demonstrated significant improvements in average speed.
  • The model showed enhanced time efficiency in obstacle avoidance tasks.
  • The proposed method proved more effective in complex environments than traditional flocking control models.
  • The model successfully predicted paths for mobile obstacles and navigated concave obstacles.

Conclusions:

  • The new cooperative obstacle avoidance model enhances mobile WSN performance in target tracking.
  • The model offers superior adaptability and efficiency for obstacle avoidance in complex environments.
  • This approach provides a more robust solution compared to traditional flocking control methods.