Robotic Fall Detection Using Object Location Probability Maps
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Solution Overview
Problem
Conventional object detection systems face limitations in accurately detecting out-of-place objects and require improved methods to differentiate between true and false object location estimates, leading to increased resource usage and errors.
Innovation Solution
A method and system that utilize a probability distribution of object locations over time, generated through clustering analysis and density-based clustering, to identify high and low probability areas, enabling a robotic device to perform actions when an object is found in unlikely locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object detection systems store each estimated object location with timestamp in a database, then object locations can be tracked over time, but the system cannot accurately distinguish between true and false object location estimates, leading to increased errors
Solution Approach 1:
The system performs preliminary clustering analysis on historical object location data to establish expected location patterns and probability distributions before making detection decisions. This allows the system to pre-characterize normal object locations and quickly identify anomalies without extensive real-time computation.
Solution Approach 2:
The system uses previously detected object locations and clustering results as feedback to improve future detections. By continuously updating the probability distribution of object locations based on historical data, the system refines its ability to distinguish true detections from false positives over time.
2Reliability
If the system performs comprehensive object detection and tracking, then it can monitor all objects in the environment, but resource usage increases due to processing and storing all location data
Solution Approach 1:
The system extracts only the essential information needed for detection - the probability distribution of object locations - from the comprehensive set of all detected object locations. By focusing computational resources on maintaining this distilled representation rather than processing every individual detection, the system reduces resource usage while preserving detection effectiveness.
Solution Approach 2:
The system transforms the raw object location data into a probability distribution parameterization, changing the representation from individual coordinate pairs to statistical parameters. This parameter transformation reduces the dimensional complexity of the data and enables more efficient processing and storage.
3Productivity
If the system uses simple object detection without probability analysis, then the system is computationally efficient, but it cannot effectively identify out-of-place objects or reduce false positives
Solution Approach 1:
The system pre-computes clustering parameters and probability distributions from historical data, storing these as reference models. During real-time operation, the system only needs to compare new detections against these pre-established models, enabling fast detection decisions without extensive real-time computation.
Solution Approach 2:
The system applies probability analysis selectively - performing full clustering and probability calculation only when needed to establish baseline patterns, and using simpler comparison operations for routine detections. This partial application of complex analysis maintains speed while improving reliability where it matters most.
Data Source
AI summary
A method for controlling a robotic device based on observed object locations is presented. The method includes observing objects in an environment. The method also includes generating a probability distribution for locations of the observed objects. The method further includes controlling the robotic device to perform an action when an object is at a location in the environment with a location probability that is less than a threshold.