Robotic Action Monitoring via Map-Based Tracking of Unsensored Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for monitoring movements and actions, such as SLAM and visual multimedia content monitoring, face challenges in tracking non-computerized objects like animals or people, as they require objects to be equipped with sensors, and struggle to accurately monitor actions or progress of activities.
Innovation Solution
A method and system that utilize a localization device with sensors to detect motion, correlate it with known patterns, and create a heat map to track object locations and actions, allowing for effective monitoring of movements and actions without the need for sensors on the tracked objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SLAM techniques are used to track objects, then tracking capability is improved, but the requirement for sensors on tracked objects increases device complexity
Solution Approach 1:
The patent introduces a camera as an intermediary device to track objects without requiring sensors on the objects themselves. The camera captures images, and a processor identifies object locations and movements by analyzing these images, thereby mediating between the tracking need and the objects being tracked.
Solution Approach 2:
The system creates a digital representation (map) of the environment and objects within it. By copying the physical environment into a digital model, the system can track and analyze object movements without physically interacting with or equipping the objects with sensors.
2Ease of operation
If visual multimedia content monitoring is used to track non-computerized objects, then ease of operation is improved, but measurement precision of actions deteriorates
Solution Approach 1:
The system performs preliminary actions by creating a detailed map of the environment and identifying objects of interest before actual tracking begins. This pre-processing establishes a framework that enables more accurate and efficient action monitoring during operation.
Solution Approach 2:
The system continuously analyzes images to detect object movements and actions, providing feedback about what is happening in the monitored space. This feedback loop enables the system to identify and track specific actions with greater precision by comparing current states against the established environmental model.
3Measurement precision
If visual monitoring is used to determine object paths, then tracking capability is improved, but the ability to monitor actual actions deteriorates
Solution Approach 1:
The system segments the monitoring task into distinct components: path tracking and action identification. By separating these functions, the system can accurately track object paths while simultaneously analyzing specific actions, preventing the loss of action progress information that occurs in unified visual monitoring approaches.
Solution Approach 2:
The system adds a temporal dimension to the monitoring by analyzing sequences of images over time. This enables the system to not only track positions but also identify and monitor actions by detecting changes in object states and behaviors across multiple time points.
Data Source
AI summary
Systems and methods for monitoring movements. A method includes detecting motion based on first localization data related to a localization device moving in a distinct motion pattern, wherein the first localization data is based on sensor readings captured by at least one sensor; correlating the detected motion to a known motion of the localization device based on respective times of the first localization data and of the localization device; localizing the localization device with respect to a map based on the correlation; tracking at least one first location of an object based on second localization data captured by the at least one sensor, wherein the at least one first location is on the map, wherein the tracking further comprises identifying at least one second location of the object based on the second localization data and determining the at least one first location based on the at least one second location.