Modular Robotic Platform With Neural Network Control
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Solution Overview
Problem
Current robotic control systems are limited by their size, weight, and architecture due to unnecessary auxiliary components, requiring significant engineering skill and complex programming paradigms, making them inaccessible to users without technical expertise.
Innovation Solution
A modular robotic device with an intuitive control system that allows users to select and configure modules for specific functions, using artificial neural networks to control robotic components, enabling easy reconfiguration and behavioral training without extensive knowledge in robotics or programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional controller boards or boxes are used, then computing power and functionality are provided, but the system requires significant engineering skill and complex programming, making it inaccessible to users without technical expertise
Solution Approach 1:
The neural network controller performs behavioral control automatically through learned patterns rather than requiring manual programming. The system trains itself to control robotic behaviors, eliminating the need for users to write complex control code and making the system accessible to non-programmers.
Solution Approach 2:
Traditional mechanical programming approaches (writing code, configuring controllers) are replaced with a biological-inspired neural network system that learns behaviors through training data. This substitution transforms the control paradigm from manual programming to automated learning.
2Ease of manufacture
If traditional robotic control systems are used, then control functionality is achieved, but the system requires auxiliary components and significant engineering effort
Solution Approach 1:
The patent integrates the neural network controller, power management, and control functionality into a single unified controller box. This consolidation eliminates the need for separate auxiliary components and reduces system complexity while maintaining full functionality.
Solution Approach 2:
The controller box is designed as a universal platform that can control various robotic configurations and behaviors through the neural network. The same controller handles power management, sensor processing, and actuator control, reducing the need for specialized components for each function.
3Adaptability or versatility
If modular robotic systems are used, then reconfigurability and customization are improved, but the system size and weight may increase due to multiple components
Solution Approach 1:
The robotic system is divided into modular functional units (sensors, actuators, power packs) that can be selectively assembled. The controller box serves as a central hub that manages these modules, allowing users to create customized robotic configurations without requiring a complete system for each application.
Solution Approach 2:
The patent extracts the essential control functionality into a compact controller box that can be separated from the robotic body. This allows the control system to be reused across different robotic configurations, reducing the overall weight and component count compared to having dedicated control systems for each robot.
Data Source
AI summary
Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.