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Trajectory planning of quadrotor elastic suspension system based on DDPG algorithm.

Zhikun Wang1, Sen Yang1, Tian Xie1

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ISA Transactions
|November 10, 2025
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Summary

This study introduces a new method for controlling unmanned aerial vehicles (UAVs) with elastic suspension systems. The approach reduces load swing and path length, improving flight efficiency and stability.

Keywords:
DDPGElastic suspensionQuadrotorReinforcement learningTrajectory planning

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Area of Science:

  • Robotics
  • Control Systems
  • Aerospace Engineering

Background:

  • Current research often overlooks the dynamics of elastic ropes in UAV payload systems.
  • Traditional control algorithms can lead to suboptimal performance, including increased load swing and path length.

Purpose of the Study:

  • To develop and validate an advanced control strategy for quadrotor UAVs with elastically suspended payloads.
  • To address limitations of existing methods by considering rope elasticity and optimizing flight trajectories.

Main Methods:

  • Established a three-dimensional dynamic model for the elastic suspension system.
  • Designed a reward function for Deep Deterministic Policy Gradient (DDPG) algorithm.
  • Utilized Long Short-Term Memory (LSTM) networks and prioritized experience replay for training.

Main Results:

  • Simulations in complex environments demonstrated superior performance compared to traditional algorithms.
  • Reduced load swing angles and path lengths were achieved.
  • Real-time flight experiments validated the effectiveness of the proposed method.

Conclusions:

  • The developed DDPG-based approach effectively controls UAVs with elastic suspension systems.
  • This method offers significant improvements in payload stability and trajectory efficiency.
  • The findings have implications for advanced aerial robotics and logistics.