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Related Experiment Video

Updated: Jul 15, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network.

Juncao Hong1, Diquan Chen1, Wenji Li1,2,3

  • 1College of Engineering, Shantou University, Shantou 515063, China.

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|September 28, 2023
PubMed
Summary

This study introduces a novel trajectory planner for quadrotor drones, utilizing a kinodynamic gene regulation network potential field. This approach enhances flight safety and efficiency in complex environments by integrating environmental data with drone dynamics.

Keywords:
environmental perceptiongene regulation networkkinodynamic constraintspotential fieldtrajectory planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Quadrotor unmanned aerial vehicles (UAVs) face significant challenges in real-world applications due to complex environments and dynamic limitations.
  • Effective trajectory planning is crucial for ensuring the safety and efficiency of UAV operations.

Purpose of the Study:

  • To develop an innovative quadrotor trajectory planner that integrates environmental perception with the UAV's dynamic state.
  • To address the limitations of existing methods by incorporating kinodynamic constraints into a potential field framework.

Main Methods:

  • A novel kinodynamic gene regulation network (K-GRN) potential field was developed.
  • The K-GRN potential field enhances the adaptability of the gene regulation network (GRN) model to UAV dynamics and environmental surroundings.
  • The trajectory planner utilizes this K-GRN to generate safe and efficient flight paths.

Main Results:

  • The proposed trajectory planner successfully guides quadrotor UAVs through intricate environments.
  • Generated trajectories effectively account for dynamic constraints, improving adaptability and stability.
  • Empirical results demonstrate the methodology's efficacy.

Conclusions:

  • The K-GRN potential field approach offers a robust solution for quadrotor trajectory planning.
  • This method enhances UAV performance in complex and dynamic scenarios.
  • The integration of environmental insights and kinodynamic constraints leads to superior trajectory generation.