Robot Controller Self-Reconfiguration for Task-Specific DL Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modular robots require external intervention for reconfiguration to adapt to new tasks, limiting their flexibility and efficiency.
Innovation Solution
A self-reconfigurable robot controller (RCS) with self-awareness capabilities to determine necessary tools and equipment, and a controller self-generation capability to adapt its deep learning model for new tasks without external intervention, using functional semantic identifiers and deep learning models to correlate user commands with peripheral device functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modular robots use fixed controller hardware and software for specific tasks, then task execution reliability is improved, but adaptability to new tasks deteriorates
Solution Approach 1:
The controller system transitions from a static, fixed configuration to a dynamic, self-reconfigurable architecture. The processing circuitry automatically generates new controller software and reconfigures hardware resources in real-time based on task requirements, enabling the robot to adapt to different tasks while maintaining reliable execution through systematic controller synthesis.
Solution Approach 2:
The robot controller performs self-reconfiguration without external intervention. The processing circuitry autonomously generates controller software, allocates hardware resources, and manages the transition between tasks, eliminating the need for manual reprogramming or external control system modifications.
2Device complexity
If modular robots require external intervention for reconfiguration, then device complexity is reduced, but productivity and efficiency deteriorate
Solution Approach 1:
The controller system autonomously performs reconfiguration operations including software generation, hardware resource allocation, and system state management without external intervention, dramatically improving reconfiguration efficiency and productivity while maintaining manageable complexity through automated processes.
Solution Approach 2:
The system pre-loads multiple controller software variants and hardware configuration profiles into memory during manufacturing or initial setup. During operation, the processing circuitry rapidly switches between pre-prepared configurations or generates new ones on-demand, reducing real-time reconfiguration complexity and execution time.
3Device complexity
If the robot uses a single deep learning model for all tasks, then device complexity is reduced, but task-specific performance and precision deteriorate
Solution Approach 1:
The controller system divides the deep learning model into multiple task-specific sub-models or modules. The processing circuitry dynamically selects and activates only the relevant sub-models required for the current task, improving control precision for each specific task while managing overall system complexity through selective activation and modular architecture.
Data Source
AI summary
A self-reconfigurable controller for a robot, including: input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and processing circuitry that is operable to: register the I/O peripheral devices and associated functionalities; receive a command for the robot to perform a task; conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices; and generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task.