RESEARCH PAPER
Optimal Trajectory Planning Method for Handling Robots Based on Multi-objective Particle Swarm Optimization Guided by Evolutionary Information
1
Henan Polytechnic University, China
2
Muroran Institute of Technology, Japan
3
Nantong University, China
Submission date: 2024-12-02
Final revision date: 2025-01-09
Acceptance date: 2025-03-16
Online publication date: 2025-03-23
Publication date: 2025-03-23
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):202990
HIGHLIGHTS
- A novel EIGMOPSO optimizes handling robot trajectories for time, energy, and impact.
- Proposes a regionally dynamic stratification strategy enhancing performance stability.
- Designs region-guided layered optimization to maintain diversity and global search.
- Develops a two-stage archive strategy ensuring solution uniformity and convergence.
KEYWORDS
Particle swarm optimizationMulti-objective optimizationTrajectory planningHandling robotEvolutionary information guidance
TOPICS
ABSTRACT
This paper addresses the trajectory optimization and reliability challenges of 6-DOF handling robots by proposing a multi-objective particle swarm optimization method guided by evolutionary information (EIGMOPSO). The method optimizes trajectory planning in terms of time, energy consumption, and smoothness to enhance operational reliability and mechanical durability. To overcome the limitations of traditional MOPSO, a regionally dynamic stratification strategy based on evolutionary capability assessment is proposed, classifying the population into regions by evaluating fitness, diversity, and stability. A layered optimization mechanism dynamically adjusts exploration and exploitation processes, improving global search capability. Additionally, a dynamic two-stage archive maintenance strategy ensures high-quality solutions. Experimental results demonstrate that EIGMOPSO significantly improves operational efficiency, reduces mechanical wear and energy consumption, and enhances system maintainability, making it well-suited for handling robots in industrial environments.
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