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Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning.

Feng Zhang¹, Leijun Li², Peiquan Xu^1,3

¹School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This summary is machine-generated.

This study introduces advanced algorithms for robotic navigation, enhancing high-precision positioning and obstacle avoidance. The improved methods ensure safer, smoother robot movement and efficient multi-target detection in complex environments.

Keywords:

TEB algorithm cartographer algorithm inspection robot iterative closest point (ICP)

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

High-precision positioning and multi-target detection are crucial for robotic path planning and obstacle avoidance.
Existing algorithms often face challenges in accuracy and efficiency in dynamic environments.

Purpose of the Study:

To develop and validate an integrated system for high-precision robot positioning and efficient multi-target detection.
To enhance robotic path planning and obstacle avoidance capabilities.

Main Methods:

Utilized the Cartographer algorithm for map generation.
Combined iterative nearest point (ICP) and occupation probability algorithms for point cloud scanning and matching.
Implemented Sparse Matrix Pose Optimization for enhanced positioning accuracy.
Developed an improved timing elastic band (TEB) algorithm for safe and smooth trajectory generation, incorporating a critical factor for waypoint adjustment and speed constraints.

Main Results:

Achieved positioning accuracy within 5 cm (x, y) and controlled angle error within 2°.
Reduced positioning time by 40%.
Demonstrated successful navigation around multiple obstacles, with the robot selecting paths with fewer obstacles.
Ensured smooth robot movement during turns and approach to the target, minimizing overshoot.

Conclusions:

The proposed integrated system, combining Cartographer, ICP, Sparse Matrix Pose Optimization, and an improved TEB algorithm, is effective for high-precision positioning.
The enhanced TEB algorithm enables efficient multi-target detection and safe, smooth robot navigation in complex environments with obstacles.