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Related Experiment Video

Updated: May 14, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

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Density-Based Detection Rapid Exploration Random Tree for Multirobot Formation Cooperative Path Planning.

Yuzhuo Shi¹, Yang Yang², Jinjun Liu³

¹College of Information Technology, Tianjin College of Commerce, Tianjin 300350, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study introduces a novel multi-robot formation path planning method using density detection rapidly exploring random tree (DDRRT) and artificial potential fields (APF) for efficient navigation and obstacle avoidance.

Keywords:

RRT algorithm artificial potential field method consistency control density testing multirobot formation

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Multirobot systems require sophisticated path planning for coordinated movement.
Existing methods often struggle with complex environments and dynamic obstacle avoidance.

Purpose of the Study:

To develop an advanced path planning strategy for multirobot formations.
To enhance navigation efficiency and obstacle avoidance capabilities in complex environments.

Main Methods:

Density Detection Rapidly Exploring Random Tree (DDRRT) for global path generation.
Optimized Artificial Potential Field (APF) with rotational potential for local obstacle avoidance.
Consistency control and polar coordinate transformation for formation control and dynamic adjustments.

Main Results:

The DDRRT algorithm effectively generates optimal global paths, avoiding redundant exploration.
The enhanced APF successfully navigates multirobot formations around obstacles, mitigating local oscillations.
The proposed formation control and transformation mechanisms improve overall system maneuverability.

Conclusions:

The integrated approach provides high-quality paths for multirobot formations in cluttered environments.
The strategy enables rapid and effective avoidance of diverse local obstacles through adaptive formation changes.