AI-Generated Executable Control for Adaptive Robot Nodes
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Solution Overview
Problem
Conventional robots struggle to adapt to unforeseen circumstances due to limitations in design, programming, sensor capabilities, and processing power, leading to a lack of real-time decision-making and reliance on human supervision for complex tasks.
Innovation Solution
A system utilizing an artificial intelligence generated machine executable (AGMX) platform that autonomously generates and implements machine executable files, enabling robots to process sensor inputs, adjust instructions, and enforce management authority protocols to adapt to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional robots use fixed programming and sensor capabilities, then device complexity is reduced, but adaptability to unforeseen circumstances deteriorates
Solution Approach 1:
The system dynamically generates and updates executable instructions in real-time based on sensor inputs and environmental conditions. The AI model continuously adapts the robot's behavior by modifying executable code, transforming a static system into a dynamic one that can respond to unforeseen circumstances without requiring complex pre-programming for every scenario.
Solution Approach 2:
The robot system performs self-modification of its executable instructions through the AI model. Instead of requiring external reprogramming for each new situation, the system autonomously generates updated instructions based on sensor data and environmental feedback, enabling self-adaptation without increasing hardware complexity.
2Productivity
If robots rely on human supervision for complex tasks, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The system implements a feedback loop where sensor data from the environment is continuously fed into the AI model, which then generates updated executable instructions. This closed-loop feedback mechanism enables the robot to autonomously make real-time decisions and adjustments, improving productivity while maintaining reliability through continuous environmental monitoring and adaptive response.
Solution Approach 2:
The patent replaces the mechanical system of human supervision with an AI-based automated decision-making system. The AI model substitutes human operators by processing sensor inputs and generating executable instructions, thereby eliminating the need for continuous human oversight while maintaining or improving both productivity and reliability.
3Speed
If robots lack real-time decision-making capability, then device complexity is reduced, but speed of response deteriorates
Solution Approach 1:
The system performs preliminary action by pre-compiling and storing multiple executable instruction sets that can be rapidly deployed in response to different environmental conditions. The AI model has pre-trained knowledge and can quickly select or generate appropriate instructions without requiring complex real-time computation, enabling fast response speeds while managing device complexity through prepared instruction libraries.
Data Source
AI summary
A system for autonomously generating an implementing a machine executable file is disclosed herein. The system can include a node and an artificial intelligence generated machine executable (“AGMX”) platform communicatively coupled to the node. The AGMX platform can include a control circuit and a memory to store an artificial intelligence model that, when executed by the control circuit, causes the AGMX platform to receive a user input including an objective, generate an instruction for the node to execute based on the objective, determine a management authority protocol to be assigned to the node, wherein the management authority protocol defines an ability of the node to deviate from the instruction, generate an AGMX file including the instruction and the management authority protocol, wherein the AGMX file is to be executed by the node, and transmit the AGMX file to the node for execution by the node.