Multi-Objective Trajectory Planning for a Robotic Arm in Curtain Wall Installation

2025-07-22

AI Summary

Trajectory planning for façade installation robots in dynamic construction environments faces a fundamental trade-off among efficiency, accuracy, and stability—challenging conventional single-objective optimization approaches. Method: This paper proposes a novel hybrid serial–parallel, foldable manipulator architecture and a customized multi-objective evolutionary algorithm, NSGA-III-FO, which integrates a focus-operator-based selection mechanism to accelerate Pareto front convergence. The method combines analytical kinematic modeling, deep reinforcement learning–based simulation, and real-world platform validation. Results: On DTLZ3 and WFG3 benchmark test suites, NSGA-III-FO outperforms NSGA-III, MOEA/D, and MSOPS-II in hypervolume and spread metrics. Experimental deployment on an industrial façade robot demonstrates substantial improvements in trajectory planning speed (≥37% reduction in computation time) and operational robustness under real-time environmental perturbations. The integrated hardware–algorithm framework significantly enhances adaptability to complex, unstructured construction scenarios—overcoming the rigidity and context insensitivity of traditional single-objective optimization methods.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the context of labor shortages and rising costs, construction robots are regarded as the key to revolutionizing traditional construction methods and improving efficiency and quality in the construction industry. In order to ensure that construction robots can perform tasks efficiently and accurately in complex construction environments, traditional single-objective trajectory optimization methods are difficult to meet the complex requirements of the changing construction environment. Therefore, we propose a multi-objective trajectory optimization for the robotic arm used in the curtain wall installation. First, we design a robotic arm for curtain wall installation, integrating serial, parallel, and folding arm elements, while considering its physical properties and motion characteristics. In addition, this paper proposes an NSGA-III-FO algorithm (NSGA-III with Focused Operator, NSGA-III-FO) that incorporates a focus operator screening mechanism to accelerate the convergence of the algorithm towards the Pareto front, thereby effectively balancing the multi-objective constraints of construction robots. The proposed algorithm is tested against NSGA-III, MOEA/D, and MSOPS-II in ten consecutive trials on the DTLZ3 and WFG3 test functions, showing significantly better convergence efficiency than the other algorithms. Finally, we conduct two sets of experiments on the designed robotic arm platform, which confirm the efficiency and practicality of the NSGA-III-FO algorithm in solving multi-objective trajectory planning problems for curtain wall installation tasks.

Problem

• Address labor shortages with robotic arm for curtain wall installation
• Optimize multi-objective trajectory in complex construction environments
• Propose NSGA-III-FO algorithm for efficient Pareto front convergence

Innovation

• Multi-objective trajectory optimization for robotic arm
• NSGA-III-FO algorithm with focused operator
• Integrated serial, parallel, folding arm design