Detection System Self-Configuration for Adaptive Perception Planning
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Solution Overview
Problem
Conventional automation systems with autonomous functions lack flexibility and efficiency in dynamic environments, requiring significant engineering effort for perception system design and unable to adapt to new tasks or situations effectively.
Innovation Solution
An automatic configuration system that dynamically plans action sequences for perception goals, utilizing a world model with sensor data collection, fusion, and evaluation algorithms to optimize resource use and adapt to new tasks with minimal effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perception systems are manually configured by engineers with predefined routines, then system reliability is improved, but device complexity and engineering effort increase significantly
Solution Approach 1:
The system performs self-configuration through autonomous planning of perception actions. The planning unit automatically generates action sequences to achieve perceptual goals without engineer intervention, and the simulation unit validates these sequences. This self-service mechanism eliminates manual configuration complexity while maintaining reliability through automated verification.
Solution Approach 2:
The system transitions from static predefined routines to dynamic action sequence planning. The planning unit generates flexible action sequences adapted to current perceptual goals and environmental conditions, allowing the system to dynamically adjust its perception strategy rather than following rigid predetermined patterns.
2Stability of the object's composition
If perception systems operate with predefined routines, then system stability is improved, but adaptability to new tasks deteriorates
Solution Approach 1:
The system replaces static predefined routines with dynamic action sequence planning that adapts to new perceptual goals. The planning unit generates task-specific action sequences based on current requirements, enabling the system to adapt to diverse perception tasks while maintaining stability through the structured planning and simulation framework.
Solution Approach 2:
The system performs preliminary simulation of action sequences before actual execution. The simulation unit validates planned actions in a virtual environment, ensuring stability and correctness before deploying to the real perception system, thus maintaining system stability while enabling task adaptability.
3Measurement precision
If comprehensive sensor data collection is performed, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system performs partial data collection by selecting only the necessary sensors and actions required to achieve the current perceptual goal. The planning unit determines the minimal sufficient set of perception actions needed, avoiding unnecessary energy consumption from comprehensive data collection while maintaining adequate measurement precision for the specific task.
Solution Approach 2:
The system dynamically adjusts the scope of data collection based on current perceptual goals and environmental conditions. The planning unit generates adaptive action sequences that activate only the relevant sensors needed for the current task, optimizing the balance between measurement precision and energy consumption rather than continuously collecting comprehensive data.
4Productivity
If multiple action sequences are planned and simulated, then productivity is improved, but computational load increases
Solution Approach 1:
The system plans and simulates only a limited number of relevant action sequences rather than exhaustively evaluating all possible sequences. The planning unit identifies and prioritizes the most promising action sequences based on the current perceptual goal, performing simulation on a selective subset that is sufficient to achieve the goal efficiently without overwhelming computational resources.
Data Source
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AI summary
Automatic configuration system (1) for a detection system, having: . a modelling unit (10) for providing a world model (12) comprising a semantic data model of the detection system and a description of an area, wherein the area comprises the detection system and surroundings of the detection system, . a state unit (20) for determining a present state (Z0) of the world model (12), . a planning unit (30) for planning a sequence (S) of actions (A) from a set of determined actions (A) for achieving a determined detection aim (Z) on the basis of the present state (Z0), wherein the determined actions (A) of the set comprise capturing sensor data, combinating captured sensor data, historic data and/or world model data to provide combined data, and evaluating the combined data, and . a simulation unit (40) for simulating execution of the planned sequence (S). Method for operating the configuration system, autonomous system having the detection system and computer program product.