Hierarchical Robot Master Control for Coordinated Dual-Robot Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The operation efficiency of a single robot is low, and existing technologies face challenges in coordinating multiple robots to improve overall efficiency, particularly in object detection and control tasks.
Innovation Solution
A robot master control system is introduced, featuring a master controller that coordinates dual-robot control systems, utilizing a self-designed neural network for improved object boundary recognition and a hierarchical convolutional network for enhanced feature extraction, along with an attention-based coding process to optimize convergence speed and generate coordinated instruction sequences for the robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots are coordinated to operate, then operation efficiency is improved, but control complexity increases
Solution Approach 1:
The control system is segmented into a master controller that handles high-level coordination and multiple slave controllers that handle individual robot control. This division allows complex multi-robot coordination to be managed through hierarchical decomposition, improving efficiency without overwhelming the control architecture.
Solution Approach 2:
The master controller acts as an intermediary between the central control system and individual slave controllers. It receives coordination commands, processes them through neural networks for decision-making, and distributes appropriate instructions to slave controllers, simplifying the overall control complexity while maintaining high productivity.
2Measurement precision
If neural network processing is used for object detection, then recognition accuracy is improved, but computation time increases
Solution Approach 1:
The system performs preliminary action by pre-processing images to extract key features before neural network classification. The master controller identifies regions of interest and pre-processes them, reducing the computational burden on the neural network and accelerating overall object detection while maintaining accuracy.
Solution Approach 2:
The system applies partial action by using simplified neural network models for common object categories and reserving full neural network processing only for ambiguous or critical cases. This selective approach reduces average computation time while preserving recognition accuracy when needed.
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
The present disclosure relates to a robot master control system. The robot master control system includes: a master controller, configured to control at least one dual-robot control system, where each of the least one dual-robot control system includes a first robot, a second robot, and a sub-controller controlling the first robot and the second robot, and the sub-controller is controlled by the master controller. In the present disclosure, multiple robots may be coordinated and comprehensively controlled to grab and move objects. Compared with a single robot, the efficiency of the multiple robots operation is greatly improved. In addition, each dual-robot control system may be individually configured, thereby improving the work efficiency of coordinated work of dual-robot control systems.