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Related Experiment Video

Updated: Aug 17, 2025

Operation of the Collaborative Composite Manufacturing CCM System

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Published on: October 1, 2019

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot.

Ana Šelek¹, Marija Seder¹, Mišel Brezak¹

¹Laboratory for Autonomous Systems and Mobile Robotics (LAMOR), Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This study introduces a novel complete coverage path planning algorithm using clothoids for nonholonomic mobile robots. The algorithm optimizes paths to reduce time, length, and overlap, enhancing coverage efficiency.

Keywords:

complete coverage mobile robot path planning path smoothing velocity profile optimization

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Area of Science:

Robotics
Computational Geometry

Background:

Complete coverage path planning (CCPP) is crucial for mobile robots to survey or clean environments.
Existing CCPP algorithms often result in suboptimal paths with significant overlap and long traversal times.

Purpose of the Study:

To develop a novel CCPP algorithm that generates smooth, time-optimal paths for nonholonomic mobile robots.
To improve coverage efficiency by reducing path length, overlap, and overall time.

Main Methods:

The proposed algorithm utilizes clothoids to generate smooth paths for nonholonomic robots.
Paths are optimized for minimal time, length, and overlap, enhancing coverage rate.
The algorithm is designed for computational simplicity, enabling real-time operation and replanning.

Main Results:

Simulations demonstrate significant reductions in coverage time, path length, and overlap area compared to state-of-the-art methods.
The coverage rate is substantially increased.
Experimental validation on a Pioneer 3DX robot confirms the algorithm's efficiency and effectiveness.

Conclusions:

The clothoid-based CCPP algorithm offers a superior approach for efficient and time-optimal environment coverage.
Its computational simplicity makes it suitable for real-time applications and dynamic environments with unknown obstacles.