AI Control Platform for Adaptive Autonomous Trajectory Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous systems in the Automation Era require manual programming and lack adaptive methods, AI instrumentation, and learning algorithms, leading to high validation cycles and costs, as well as inefficiencies in decision-making and resource utilization.
Innovation Solution
A control platform that integrates digital product and process information with sensor data using artificial intelligence and neural networks for autonomous system feedback and decision-making, enabling automatic trajectory generation, self-protection, and optimization of processes through historical data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual programming and strict trajectory following are used, then system control is simplified, but adaptability to environmental changes and errors is lost
Solution Approach 1:
The system implements feedback mechanisms where sensors continuously monitor the physical environment and feed this information back to the control platform. This enables the system to detect deviations from planned trajectories and environmental changes, then automatically adjust its behavior without requiring complex manual reprogramming.
Solution Approach 2:
The autonomous system performs self-service through automatic trajectory generation and adjustment capabilities. The control platform independently processes sensor data, makes decisions about trajectory modifications, and executes corrections without human intervention, reducing the need for complex manual control while maintaining adaptability.
2Object-affected harmful factors
If virtual environment simulation is used for programming, then safety is improved, but representativeness of the physical environment decreases
Solution Approach 1:
The system performs preliminary actions by generating and validating trajectories in a virtual environment before executing them in the physical world. This allows safety testing and optimization to occur in a controlled simulation, reducing risks during actual deployment while maintaining environmental representativeness through accurate sensor feedback.
Solution Approach 2:
The system creates a digital twin or virtual copy of the physical environment for simulation purposes. This virtual model is continuously updated with real sensor data to maintain representativeness, allowing safe validation while preserving accuracy of environmental representation through ongoing synchronization with the physical system.
3Adaptability or versatility
If trial and error methods are used for parameterization, then system flexibility is maintained, but validation time and cost increase
Solution Approach 1:
The system replaces manual trial-and-error parameterization with automated artificial intelligence algorithms. These AI systems analyze historical process data and sensor information to automatically optimize parameters, eliminating the need for time-consuming manual trial and error while maintaining flexibility through adaptive learning capabilities.
Solution Approach 2:
The system implements feedback loops that continuously monitor process outcomes and use this information to automatically adjust parameters. Historical data is fed back into the AI algorithms, enabling the system to learn from past performance and optimize parameters without requiring repeated manual trial and error cycles, thus reducing validation time while maintaining adaptability.
4Extent of automation
If AI and learning algorithms are integrated, then autonomous decision-making is improved, but device complexity increases
Solution Approach 1:
The system segments AI functionality into modular components distributed across the autonomous system. Rather than implementing a single complex centralized AI, the control platform divides decision-making functions into separate modules that process specific aspects of autonomous operation, reducing overall system complexity while maintaining high-level autonomous capabilities.
Data Source
AI summary
A control platform for autonomous systems integrates digital product and process information with data obtained by means of sensors, using artificial intelligence, for example, artificial neural networks for system feedback and decision making in the autonomous execution of activities.