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Adaptive Step RRT*-Based Method for Path Planning of Tea-Picking Robotic Arm.

Xin Li1, Jingwen Yang1, Xin Wang1

  • 1Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

A new Adaptive Step RRT* (AS-RRT*) algorithm improves path planning for tea-picking robots. This method enhances autonomy, safety, and efficiency by reducing path length and planning time, ensuring real-time obstacle avoidance.

Keywords:
AS-RRT* algorithmlocal obstacle avoidancepath planningrobotic armtea picking

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Agricultural Engineering

Background:

  • Tea-picking robots face challenges in autonomy, safety, and efficiency.
  • Path planning is crucial for effective robotic operation in complex environments like tea plantations.

Purpose of the Study:

  • To develop an advanced path planning algorithm for tea-picking robotic arms.
  • To enhance the efficiency, safety, and autonomy of tea-picking robots.

Main Methods:

  • Proposed the Adaptive Step RRT* (AS-RRT*) algorithm.
  • Employed an accumulator-based sampling point selection strategy.
  • Integrated fast connectivity, pruning optimization, dynamic step length adjustment, redundant node removal, and curve smoothing.
  • Utilized depth vision sensors for 3D environmental data acquisition.

Main Results:

  • AS-RRT* reduced path length by 14.18%.
  • Path planning time was consistently under 1 second.
  • Demonstrated enhanced obstacle avoidance capabilities in real-time.

Conclusions:

  • The AS-RRT* algorithm significantly improves path planning efficiency and obstacle avoidance for tea-picking robots.
  • The proposed method addresses key limitations in current tea-picking robot technology.
  • This advancement contributes to more autonomous and efficient agricultural robotics.