Spatio-temporal Awareness Engine for Drone Target Recovery
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Solution Overview
Problem
Autonomous robotic systems (ARS) face challenges in maintaining spatial and temporal awareness due to limited computing resources and power supplies, leading to difficulties in tracking objects that move out of their field of view, requiring efficient and non-computationally intensive recovery methods.
Innovation Solution
The system optimizes video data processing using a set of processing schemes with performance scores, dynamically selecting the most effective scheme based on resource availability and confidence levels, incorporating noise filtering and feature consolidation, and employing multimodal sensors for target recovery and path prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high performance machine vision algorithms are used, then tracking accuracy is improved, but computational resource consumption increases beyond available resources
Solution Approach 1:
The patent segments the video processing pipeline into multiple stages: initial detection phase using simplified algorithms, tracking phase using more sophisticated algorithms only when needed, and recovery phase using predictive models. This segmentation allows the system to use high-accuracy algorithms selectively rather than continuously, reducing overall computational resource consumption while maintaining tracking accuracy when required.
Solution Approach 2:
The system dynamically adjusts the complexity of processing schemes based on real-time conditions such as target visibility, motion speed, and resource availability. When resources are abundant and targets are stationary, simpler algorithms are used. When resources permit and targets are in motion or partially occluded, more sophisticated algorithms are activated, creating a dynamic balance between accuracy and resource usage.
2Reliability
If wide area sweep is performed to recover lost target, then target recovery capability is improved, but time and computational cost increase significantly
Solution Approach 1:
The patent implements preliminary action by maintaining predictive models of target motion and behavior before target loss occurs. When a target is detected, the system pre-calculates probable trajectories and maintains a search priority queue based on predicted target locations. This allows the system to quickly resume tracking after brief interruptions without performing exhaustive wide-area sweeps, significantly reducing recovery time while maintaining reliability.
Solution Approach 2:
The system introduces intermediary predictive models and probability maps that act as mediators between target detection and full visual search. These intermediaries provide probabilistic guidance for where to search next, allowing the system to focus computational resources on high-probability regions rather than performing uniform wide-area sweeps, thus improving recovery capability while reducing time and computational cost.
3Adaptability or versatility
If multiple processing schemes are maintained for different scenarios, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic processing scheme selection mechanism that automatically adapts to different operational scenarios based on real-time conditions. The system monitors target characteristics, environmental factors, and resource availability to dynamically select from multiple processing schemes, maintaining high adaptability without requiring manual configuration or complex decision-making logic, thus managing system complexity effectively.
Solution Approach 2:
The system employs self-service mechanisms where the processing pipeline automatically adjusts its own complexity and algorithm selection based on performance metrics and resource feedback. The system monitors its own computational load and target tracking performance, automatically switching between processing schemes without external intervention, thereby maintaining adaptability while keeping the control architecture relatively simple through self-regulation.
Data Source
AI summary
A spatio-temporal awareness engine combines a low-resolution tracking process and high resolution tracking process to employ an array of imaging sensors to track an object within the visual field. The system utilizes a low-resolution conversion through noise filtering and feature consolidation to load-balance the more computationally-intensive aspects of object tracking, allowing for a more robust system, while utilizing less computer resources. A process for target recovery and object path prediction in a robotic drone may include tracking targets using a combination of visual and acoustic multimodal sensors, operating a camera as a main tracking sensor of the multimodal sensors and feeding output of the camera to a spatiotemoral engine, complementing the main tracking sensor with non-visual, fast secondary sensors to assign rough directionality to a target tracking signal, and applying the rough directionality to prioritize visual scanning by the main tracking sensor.