Autonomous Inspection Robot AI Model Switching for Adaptive Sensing
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Solution Overview
Problem
Current robotic devices lack flexibility to perform multiple AI workloads within a single mission or trip, requiring pre-programmed tasks and limited sensor usage, leading to inefficiencies in adaptive navigation and sensing capabilities.
Innovation Solution
An autonomous roaming robotic device equipped with multiple sensors can dynamically select and switch between sensors and AI models based on real-time data analysis, allowing it to adaptively determine the appropriate sensors and AI models for specific tasks without human intervention, enabling intelligent decision-making and efficient task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robotic device uses pre-programmed missions with fixed sensor usage, then the device complexity is reduced and ease of operation is improved, but the adaptability and versatility of the device deteriorate
Solution Approach 1:
The robotic device dynamically selects and switches between different sensors and AI models based on real-time data analysis and mission requirements. The system transitions from static pre-programmed sensor usage to dynamic sensor selection, where the robot determines optimal sensor combinations during operation to adapt to varying inspection tasks and environmental conditions
Solution Approach 2:
The robotic device is equipped with multiple sensors (acoustic, visual, thermal, etc.) and multiple AI models that can be selectively activated. This multi-functionality allows a single device to perform diverse inspection tasks across different asset types without requiring separate pre-programmed missions for each scenario
2Adaptability or versatility
If a robotic device is equipped with multiple sensors and AI models, then the adaptability and task flexibility are improved, but the device complexity and energy consumption increase
Solution Approach 1:
Instead of activating all sensors and AI models simultaneously, the robotic device selectively activates only the necessary sensors and models required for the current inspection task. The system analyzes real-time data to determine which subset of sensors and AI models will suffice, avoiding the energy waste of running unnecessary components at full capacity
Solution Approach 2:
The system dynamically adjusts sensor and AI model activation based on mission progress and environmental conditions. Sensors are enabled or disabled in real-time based on whether their data contributes to the current inspection objectives, creating an energy-efficient adaptive operation mode
3Productivity
If a robotic device requires pre-programmed missions for each task, then the ease of operation is improved, but the productivity and operational efficiency deteriorate due to multiple trips and lack of decision-making autonomy
Solution Approach 1:
The robotic device autonomously determines which sensors to use and which AI models to apply for inferencing without human intervention. The system self-manages its own operation by analyzing real-time data, selecting appropriate inspection approaches, and making decisions about sensor activation and model selection, thereby eliminating the need for detailed pre-programming of each mission step
Solution Approach 2:
The robotic device continuously analyzes real-time sensor data and historical asset information to inform its sensor selection and AI model choice. This feedback loop enables the system to adapt its operation based on actual conditions encountered during inspection, improving productivity by avoiding unnecessary trips and actions
Data Source
AI summary
Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.