Multisensor Source Localization Under Turbulent Gas Plumes
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Solution Overview
Problem
Conventional automated gas source localization systems struggle in environments with turbulent airflow and obstacles, as they rely on a single sensor modality and fail to effectively navigate and localize gas sources due to trapped gas patches disconnected from the main plume.
Innovation Solution
An autonomous source localization system using a robotic platform equipped with multiple sensors (RGB camera, RGBD camera, LIDAR, RADAR, gas concentration, etc.) integrates visual, non-visual, and gas concentration data through a neural network architecture to create a joint embedding space, leveraging historical data and reinforcement learning for real-time decision-making and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional automated gas source localization systems use a single sensor modality to navigate and find the area with maximum concentration, then the system can operate in simple environments, but the system fails in environments with turbulent airflow and obstacles where gas patches are disconnected from the main plume
Solution Approach 1:
The patent combines multiple sensor modalities (visual sensors, non-visual sensors, and gas concentration sensors) into an integrated sensing system. The visual sensors capture images of the environment and gas plumes, non-visual sensors provide additional environmental context, and gas concentration sensors measure chemical concentrations. This multi-sensor fusion approach allows the system to reliably locate gas sources in complex environments with turbulent airflow and obstacles by cross-referencing information from different sensing modalities.
Solution Approach 2:
The robotic platform is designed with multi-functional capabilities to operate in diverse environments. It integrates navigation functions using visual data, environmental mapping using non-visual sensors, and gas source detection using concentration sensors. This universal design enables the single platform to adapt to various operational scenarios including indoor and outdoor environments with different lighting conditions, obstacle densities, and gas dispersion patterns.
2Measurement precision
If the system uses multiple sensors and neural network integration to improve source localization accuracy, then the system can handle complex environments, but the device complexity increases
Solution Approach 1:
The patent introduces a neural network as an intermediary processing layer that integrates data from multiple sensor modalities. The neural network receives visual features, non-visual environmental data, and gas concentration measurements, then processes this multi-modal information to predict gas source locations. This intermediary neural network architecture manages the complexity of fusing heterogeneous sensor data while maintaining high localization precision through learned patterns and relationships in the data.
Solution Approach 2:
The system replaces traditional mechanical or rule-based sensor fusion approaches with a data-driven neural network model. Instead of using complex hardware mechanisms or predetermined fusion rules to combine sensor inputs, the patent employs machine learning algorithms that automatically learn optimal integration strategies from training data, thereby reducing mechanical complexity while maintaining or improving measurement precision.
3Device complexity
If conventional systems rely only on gas concentration data for navigation, then the system structure remains simple, but the system cannot effectively navigate in environments with trapped gas patches far from the source
Solution Approach 1:
The patent adds visual and non-visual sensing dimensions to the traditional gas concentration-based navigation approach. Visual sensors provide spatial context and plume structure information, while non-visual sensors capture environmental features. By integrating these additional dimensional data sources with concentration measurements, the system can distinguish between trapped gas patches and the main plume, enabling more efficient navigation to the actual source without excessive search time.
Data Source
AI summary
An autonomous system for detecting, localizing, and potentially deactivating chemical threats or emissions using multiple sensing modalities and reinforcement learning techniques. The system includes visual sensors (e.g., RGB, RGBD, LIDAR), non-visual sensors (e.g., gas concentration, airflow, GPS, RADAR), a neural network architecture and processor to fuse information from different sensors, a module based on deep reinforcement learning for decision making, and a robotic interface for executing actions. The neural network extracts relevant information from sensor streams and encodes them into a joint embedding space. The module considers the current observations, historical data, and previous actions to determine the optimal action for threat localization under partially observable conditions. The system is trained in simulated environments to minimize source localization time while accounting for various constraints. The autonomous system enables effective chemical threat detection and source localization in complex, dynamic environments without endangering human operators.