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Autonomous localized path planning algorithm for UAVs based on TD3 strategy.

Zhao Feiyu1, Li Dayan2, Wang Zhengxu1

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Unmanned Aerial Vehicles (UAVs) are valuable for diverse applications.
  • Autonomous path planning for UAVs in unfamiliar environments presents significant challenges, including poor consistency and reliance on native controllers.

Purpose of the Study:

  • To investigate reinforcement learning-based autonomous local path planning for UAVs.
  • To develop a method with high autonomous decision-making capability and local portability.

Main Methods:

  • Proposed an autonomous local path planning algorithm utilizing the TD3 (Twin Delayed Deep Deterministic policy gradient) strategy.
  • Focused on local obstacle avoidance and path planning in unfamiliar environments through autonomous decision-making.

Main Results:

  • Simulations conducted on the Gazebo platform demonstrated the method's effectiveness.
  • Achieved a 93% success rate in path planning without obstacles and 92% in environments with obstacles.

Conclusions:

  • The developed method is effective for autonomous local path planning of UAVs in unfamiliar environments.
  • The approach offers enhanced autonomous decision-making and local portability for UAV navigation.