Trajectory Planning of a Curtain Wall Installation Robot Based on Biomimetic Mechanisms

2025-07-22

AI Summary

High energy consumption and operational constraints hinder the deployment of construction robots—particularly curtain wall installation robots—in real-world scenarios. Method: Inspired by human upper-limb weightlifting biomechanics, this study proposes a bio-inspired trajectory planning method that models the dynamic kinetic–potential energy exchange in human motion as a load distribution strategy for robotic manipulators. A human-like motion feature model is established by fusing electromyographic (EMG) signals with kinematic trajectory data, and particle swarm optimization (PSO) is employed to generate energy-optimal trajectories. Contribution/Results: This work pioneers the systematic integration of biological energy metabolism principles into construction robot motion control, departing from conventional rigid trajectory planning paradigms. Simulation results demonstrate a 48.4% reduction in total system energy consumption compared to baseline approaches, significantly enhancing operational endurance and task efficiency. The proposed framework establishes a novel energy-aware design paradigm for intelligent construction equipment operating in high-power-demand environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

As the robotics market rapidly evolves, energy consumption has become a critical issue, particularly restricting the application of construction robots. To tackle this challenge, our study innovatively draws inspiration from the mechanics of human upper limb movements during weight lifting, proposing a bio-inspired trajectory planning framework that incorporates human energy conversion principles. By collecting motion trajectories and electromyography (EMG) signals during dumbbell curls, we construct an anthropomorphic trajectory planning that integrates human force exertion patterns and energy consumption patterns. Utilizing the Particle Swarm Optimization (PSO) algorithm, we achieve dynamic load distribution for robotic arm trajectory planning based on human-like movement features. In practical application, these bio-inspired movement characteristics are applied to curtain wall installation tasks, validating the correctness and superiority of our trajectory planning method. Simulation results demonstrate a 48.4% reduction in energy consumption through intelligent conversion between kinetic and potential energy. This approach provides new insights and theoretical support for optimizing energy use in curtain wall installation robots during actual handling tasks.

Problem

• Reducing energy consumption in construction robots via bio-inspired design
• Optimizing robotic arm trajectory using human-like movement patterns
• Enhancing curtain wall installation efficiency with biomimetic mechanisms

Innovation

• Bio-inspired trajectory planning from human movements
• PSO algorithm for dynamic load distribution
• 48.4% energy reduction via energy conversion