Human-intelligent trajectory optimization for robotic manipulators with hybrid PSO-PS algorithm
Year: 2025
Type: article
Abstract: Industry 5.0 is driving a new era in industrial automation, where collaboration between artificial intelligence (AI) and human supervision enables the development of smarter, more adaptive, and efficient systems. Robotic trajectory generation is a clear example of this new paradigm. Metaheuristic techniques help to automatically generation of optimized trajectories improving operational efficiency. However, optimizing trajectories using just AI also presents limitations. When initiated from random paths, the optimization process becomes computationally expensive especially in complex environments. In this context, the initial input from human experts plays a crucial role: expert-defined trajectories provide structured, feasible, and context-aware starting points that guide the AI more effectively towards high-quality solutions. Thus, this work proposes a novel human-guided trajectory optimization algorithm. This way, human knowledge, Particle Swarm Optimization (PSO), and Pattern Search (PS) are efficiently combined. Results demonstrate that this approach significantly enhances robotic system performance, achieving cycle time reductions of up to 16.69% compared to expert-defined trajectories. This approach establishes a solid framework for intelligent automation in Industry 5.0, promoting the development of more efficient, sustainable, and adaptive robotic systems.
Source: Advanced Engineering Informatics
Authors: Mario Peñacoba Yagüe, Jesús Enrique Sierra García, Matilde Santos Peñas
Institutions: Universidad de Burgos, Universidad Complutense de Madrid
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,560
OpenAlex ID: W4414999324