Multi-Robot Drilling and Riveting Control for Shelter Angle Aluminum
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Solution Overview
Problem
Existing shelter production processes face inefficiencies due to manual operations, lack of intelligent perception, and rigid automatic devices that struggle with size and angle errors, leading to reduced product quality and consistency, and are labor-intensive with limited production efficiency.
Innovation Solution
A method for controlling a multi-robot collaborating intelligent drilling and riveting system that includes generating a robot scanning trace, establishing a task allocation mechanism, and performing drilling angle compensation using multiple robots to automate and adapt to splicing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual operations are used for drilling, gluing, and riveting, then flexibility in handling various shelter configurations is maintained, but operation efficiency is low and labor intensity is high
Solution Approach 1:
The patent employs dynamic task allocation mechanisms that allow the multi-robot system to adapt to different shelter configurations in real-time. The system dynamically adjusts robot assignments, drilling paths, and operation sequences based on the specific geometric parameters and structural characteristics of each shelter type, thereby maintaining flexibility while achieving high automation efficiency
Solution Approach 2:
The patent develops universal drilling and riveting robots capable of performing multiple operations (drilling, gluing, riveting) on various shelter configurations. The robots are equipped with interchangeable tools and adaptive control systems that enable them to handle different shelter types, sizes, and structural requirements, replacing multiple specialized manual operations with a single multi-functional automated system
2Extent of automation
If manual teaching programming is used to acquire coordinates, then the automatic process device can perform drilling and riveting operations, but the task preparation time is heavy and even longer than actual working time
Solution Approach 1:
The patent implements preliminary automated path planning algorithms that generate optimal drilling and riveting paths before actual operations begin. The system pre-calculates all necessary coordinates, operation sequences, and robot trajectories based on the shelter's geometric model, eliminating the need for time-consuming manual teaching programming while maintaining complete automation capability
Solution Approach 2:
The patent replaces manual teaching programming (a mechanical/operator-dependent process) with automated coordinate acquisition systems including vision sensors, laser scanners, and RFID tags. These systems automatically capture shelter geometry and calculate operation coordinates through computational algorithms, substituting human-operated mechanical programming with automated sensing and computing systems
3Extent of automation
If rigid automatic drilling devices are used, then drilling operations can be automated, but the axial forces cause deformation of the process devices and deviation from target points
Solution Approach 1:
The patent employs real-time parameter adjustment mechanisms that monitor and modify drilling parameters (force, speed, depth) during operation. The system changes operational parameters dynamically based on feedback from sensors detecting device deformation or position deviation, allowing the rigid automatic drilling device to maintain precision by adapting its parameters rather than its physical structure
Solution Approach 2:
The patent implements closed-loop feedback control systems that continuously monitor drilling position, device deformation, and applied forces. The feedback information is used to real-time adjust robot positioning, drilling parameters, and compensation values, enabling the rigid automatic drilling device to correct deviations and maintain high manufacturing precision throughout the drilling process
4Extent of automation
If existing automatic process devices are used, then drilling operations can be performed automatically, but they are hard to adapt to splicing errors and cause hole site errors that interrupt the riveting process
Solution Approach 1:
The patent employs real-time feedback mechanisms where vision systems and sensors detect actual shelter geometry and splicing errors during operation. The detected deviations are fed back to the control system, which dynamically adjusts drilling and riveting paths, positions, and parameters to compensate for splicing errors, enabling the automatic process to adapt and continue without interruption
Solution Approach 2:
The patent implements dynamic path planning and position adjustment capabilities that allow the automatic drilling and riveting devices to adapt to real-time variations in shelter geometry. The system dynamically recalculates operation paths and adjusts robot positions based on detected splicing errors, transforming a static rigid process into a dynamic adaptive one that can handle manufacturing variations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves efficient, high-quality, and intelligent installation of angle aluminum for shelters by identifying and scanning production trajectories, adjusting processing positions, and ensuring precise drilling and riveting operations.
Implementation Method 1
performing laser scanning on the angle aluminum edge to acquire a drilling position of the shelter
Data Source
AI summary
The present invention discloses a method for controlling a multi-robot collaborating intelligent drilling and riveting system for a shelter, including the following steps: generating a robot scanning trace according to a key characteristic parameter of the shelter; performing laser scanning on an angle aluminum edge to acquire a drilling position of the shelter; establishing a task allocation mechanism, establishing a drilling and riveting task propensity model according to the task allocation mechanism, and allocating automatic drilling and riveting tasks to multiple robots based on the drilling and riveting task propensity model; and establishing a drilling angle compensation value model, where automatic drilling and riveting robots perform position compensation based on the drilling angle compensation value model to complete drilling and riveting.