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Related Experiment Video

Updated: Mar 15, 2026

Operation of the Collaborative Composite Manufacturing CCM System

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Robotic Arm Trajectory Planning for Tunnel Lighting Cleaning Based on the CAW-PSO Algorithm.

Zhibin Yao¹, Taibo Song¹, Hui Li¹

¹School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This study introduces a novel Chaotic Adaptive Whale-Particle Swarm Optimization (CAW-PSO) algorithm for efficient tunnel lighting cleaning robotic arm trajectory planning. The CAW-PSO significantly reduces cleaning time, enhancing operational efficiency and driving safety.

Keywords:

chaotic mapping dynamic learning factor inertia weight robotic arm trajectory planning whale optimization algorithm

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Area of Science:

Robotics
Artificial Intelligence
Optimization Algorithms

Background:

Tunnel lighting cleaning is crucial for road safety but faces efficiency challenges.
Existing robotic arm trajectory planning methods may not be optimal for time-critical tasks.

Purpose of the Study:

To develop an efficient trajectory planning method for tunnel lighting cleaning robotic arms.
To optimize the motion time of the SIASUN GCR16-2000 robotic arm using a novel algorithm.

Main Methods:

Proposed a Chaotic Adaptive Whale-Particle Swarm Optimization (CAW-PSO) algorithm.
Integrated a tent chaotic map, linearly decreasing inertia weight, and dynamic learning factors.
Employed the whale optimization algorithm's exploitation mechanism for improved accuracy.
Utilized 3-5-3 polynomial interpolation for trajectory construction.

Main Results:

Achieved a simulation time of 3.661 s, a 69.94% reduction in motion time.
Experimental results showed a mean relative error of 1.16%, validating simulation accuracy.
The CAW-PSO algorithm demonstrated superior performance in reducing robotic arm motion time.

Conclusions:

The CAW-PSO algorithm effectively optimizes trajectory planning for tunnel lighting cleaning.
This method significantly enhances the operational efficiency of robotic arms in such tasks.
The proposed approach shows strong applicability and potential for improving tunnel maintenance safety.