Key Takeaways & Executive Findings
- •• Comprehensive review of robotic peg-in-hole assembly, covering system composition, operational theory, and compliance strategies. • Identifies limitations of traditional position-based control and the need for flexible and intelligent assembly approaches. • Analyzes passive, active, and intelligent compliant assembly methods, highlighting their respective strengths and challenges. • Outlines future research directions, emphasizing cross-modal perception fusion and robustness in dynamic environments.
Abstract
As the demands for assembly quality and efficiency increase, robot-assisted assembly applications are becoming more widespread. Peg-in-hole assembly, as a typical form of assembly, has been widely researched by scholars. Currently, robotic peg-in-hole assembly faces challenges such as complex analysis of part contact forces, difficulties in task modeling, and the failure of traditional strategies. Simply controlling the position of the robot’s end effector cannot achieve high precision, high efficiency peg-in-hole assembly. Flexible assembly, especially intelligent flexible assembly, is becoming the future development trend. So there is a lack of comprehensive reviews on robotic flexible peg-in-hole assembly. This paper first outlines the basic components of peg-in-hole assembly and summarizes the two basic operational processes of peg-in-hole assembly, along with their related theoretical foundations. We then review and analyze the research on passive compliant assembly, active compliant assembly, and intelligent flexible assembly. Finally, it presents an outlook on the future development directions of robotic peg-in-hole assembly.
1. Introduction
Assembly is the process of integrating components into complete products and verifying their compliance through testing, constituting approximately 40% of total production costs. With increasing demands for assembly efficiency and quality, particularly driven by advancements in artificial intelligence (AI) and smart manufacturing technologies, robotic assembly has emerged as a critical trend in future industrial development [1–3]. Peg-in-hole assembly, a prevalent assembly paradigm, faces limitations when traditional position-based control strategies are applied to small-clearance, high-precision components. This process necessitates adaptive adjustments of relative poses between components based on real-time contact forces, yet encounters challenges including dynamic force coupling, difficulties in modeling complex geometric constraints, and insufficient adaptability to unstructured environments. In certain scenarios, human intervention remains indispensable, significantly hindering the widespread adoption of robotic assembly technologies [4, 5].
Although notable progress has been achieved in passive compliance devices (PCDs), active impedance control (AIC), and intelligent learning algorithms, persistent bottlenecks exist in cross-modal perception fusion [6] and robustness in dynamic environments [7]. For instance, existing compliant assembly strategies are predominantly scenario-specific, lacking generalizability [8], while intelligent algorithms heavily rely on extensive simulation data with inefficient virtual-to-real migration [9]. Consequently, it is of significant theoretical and practical value for advancing robotic intelligent manufacturing by systematically organizing the technological framework of robotic peg-in-hole assembly, analyzing core challenges, and proposing interdisciplinary innovation pathways.
Current reviews on robotic peg-in-hole assembly predominantly focus on the perception-decision-regulation-collaboration technical architecture of robotic assembly systems, emphasizing coupled relationships among multidimensional technologies through discussions on robotic assembly methodologies [10–12], application domains [13–15], and human-robot collaboration [16, 17]. However, a comprehensive review systematically grounded in mechanical analysis of peg-in-hole operations remains absent. This paper conducts a review of robotic peg-in-hole assembly, including encompassing system composition, operational theory, compliance strategies, and future trends. Section 2 dissects core challenges and methodologies, while subsequent sections detail passive, active, and intelligent compliant assembly approaches, culminating in an outlook on future research directions.
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Shanglin Li, Hao Gong, Jianhua Liu, Jiakai Li, Xinjian Deng (2025). Advances in Robotic Peg-in-Hole Assembly: A Comprehensive Review. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01349-w
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Frequently Asked Questions
What is robotic peg-in-hole assembly?
Robotic peg-in-hole assembly is a fundamental assembly task where a robot inserts a peg into a hole, requiring precise alignment and force control. It is widely used in manufacturing and serves as a benchmark for robotic manipulation research.
What are the main challenges in robotic peg-in-hole assembly?
Challenges include complex contact force analysis, difficulty in task modeling, and limitations of traditional position-based control, especially for small-clearance and high-precision components. These require adaptive strategies based on real-time force feedback.
What are the different compliance strategies for peg-in-hole assembly?
The main strategies are passive compliant assembly (using mechanical devices like RCC), active compliant assembly (using impedance/admittance control), and intelligent flexible assembly (using learning-based methods). Each has its own advantages and limitations.
Why is intelligent flexible assembly considered the future trend?
Intelligent flexible assembly integrates AI and learning algorithms to adapt to unstructured environments and improve generalizability, overcoming the limitations of traditional strategies. It enables higher precision and efficiency in complex assembly tasks.
What are the future research directions in robotic peg-in-hole assembly?
Future directions include enhancing cross-modal perception fusion, improving robustness in dynamic environments, developing generalizable strategies, and advancing virtual-to-real transfer for learning-based methods.
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