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Related Experiment Video

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Operation of the Collaborative Composite Manufacturing CCM System

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Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot

Junjie Ji¹, Jing-Shan Zhao¹, Sergey Yurievich Misyurin^2,3

¹Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This study introduces precision-driven path planning for mobile robots to minimize localization errors. A novel odometry error model, validated on a robot prototype, improves path tracking accuracy in logistics and construction applications.

Keywords:

fine error estimation mobile robots multi-point path planning

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Area of Science:

Robotics
Mobile Robot Navigation
Path Planning

Background:

Multi-target path planning is critical for mobile robots and manipulators in logistics and construction.
Localization error significantly impacts the performance of multi-target path tracking.
Standard mobile platforms utilize forward movement and rotation as primary locomotion modes.

Purpose of the Study:

To propose a precision-driven multi-target path planning method.
To develop and validate a three-parameter odometry error model for mobile robots.
To enhance the accuracy of path tracking in mobile robot applications.

Main Methods:

Developed a precision-driven path planning approach based on an odometry error evaluation function.
Proposed a three-parameter odometry error model accounting for forward movement and rotation.
Validated the error model and planning method using a mobile robot prototype and OptiTrack motion capture.

Main Results:

The proposed method identifies precision-optimized paths by minimizing odometry error.
The three-parameter error model, following a normal distribution, accurately describes localization errors.
Experimental results validated the proposed error model and precision-driven path planning method.

Conclusions:

The precision-driven path planning method effectively reduces localization errors in mobile robots.
The validated odometry error model enhances the reliability of mobile robot navigation.
This research contributes to more accurate and efficient mobile robot operations in complex environments.