Maintenance automation: methods for robotics manipulation planning and execution

2025-08-25

AI Summary

Robots struggle to autonomously perform complex maintenance tasks—such as disassembly and assembly—in unstructured environments due to environmental uncertainties, particularly discrepancies between CAD models and real-world scenes. Method: This paper proposes a closed-loop autonomous execution framework integrating symbolic task planning with multimodal perception. It introduces the first approach that jointly leverages CAD prior models and real-time RGB-D sensory data to dynamically refine the symbolic planner. The framework unifies task parsing, executable instruction generation, and adaptive closed-loop control to enable end-to-end mapping from high-level intent to low-level robot actions. Results: Experimental validation in realistic maintenance scenarios demonstrates robust performance under ±5 mm pose deviations between model and reality. The system successfully executes fully autonomous disassembly and assembly operations, significantly improving reliability and generalization capability for maintenance tasks in non-structured environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Automating complex tasks using robotic systems requires skills for planning, control and execution. This paper proposes a complete robotic system for maintenance automation, which can automate disassembly and assembly operations under environmental uncertainties (e.g. deviations between prior plan information). The cognition of the robotic system is based on a planning approach (using CAD and RGBD data) and includes a method to interpret a symbolic plan and transform it to a set of executable robot instructions. The complete system is experimentally evaluated using real-world applications. This work shows the first step to transfer these theoretical results into a practical robotic solution.

Problem

• Automating robotic disassembly and assembly operations
• Addressing environmental uncertainties in maintenance tasks
• Translating symbolic plans into executable robot instructions

Innovation

• Planning approach using CAD and RGBD data
• Symbolic plan interpretation to executable instructions
• Automating disassembly and assembly under uncertainties