Object Influence Modeling for Predictive Collision Tracking
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Solution Overview
Problem
In environments where objects move about a platform, such as a ship deck, collisions between moving and stationary objects, as well as interactions with objects influencing the environment, pose challenges due to the difficulty in maintaining safe distances and avoiding potential injuries or damage, especially when individuals are preoccupied.
Innovation Solution
A computing device and method that tracks object movement by defining an object influence model with a shape and volume extending beyond the object, varying based on properties like radiation or air movement, predicts paths of travel, and identifies potential collisions using machine learning, providing alerts or actions to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If objects move freely about the platform without tracking systems, then operational efficiency is maintained, but collision risk and safety hazards increase
Solution Approach 1:
The computing device performs multiple functions including tracking object positions, predicting paths of travel, identifying potential collisions, and providing alerts. This multi-functional approach consolidates safety monitoring tasks into a single system, improving reliability without proportionally increasing complexity
Solution Approach 2:
The system predicts future paths of travel and identifies potential collisions before they occur. By performing preliminary analysis of object trajectories and generating advance warnings, the system enables preventive action rather than reactive response, enhancing safety effectiveness
2Productivity
If people are preoccupied with other tasks while moving about the deck, then task productivity is improved, but awareness of surrounding objects and collision avoidance deteriorates
Solution Approach 1:
The system continuously monitors object positions and provides real-time feedback through alerts when potential collisions are detected. This external feedback mechanism compensates for reduced human awareness, allowing workers to remain focused on tasks while the system monitors safety conditions and warns of hazards
Solution Approach 2:
The tracking system autonomously performs safety monitoring and collision detection without requiring human attention. Objects are automatically tracked and analyzed, and the system self-generates warnings when hazards are detected, freeing workers to concentrate on their primary tasks
3Reliability
If the object influence model extends beyond the physical object boundaries, then safety margins and environmental influence coverage are improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The object influence model extends the safety boundary beyond physical object dimensions to create a protective buffer zone. This local extension of the model around each object allows the system to account for environmental influences and maintain safety margins without requiring complex global modifications to the tracking algorithm
Solution Approach 2:
The system adds a conceptual dimension to object representation by incorporating the influence model that extends beyond physical boundaries. This dimensional extension allows the system to detect potential collisions and environmental interactions before physical contact occurs, improving safety without requiring higher computational dimensions
Data Source
AI summary
A computing device, method and computer program product track the movement of objects and identify potential collisions between object influence models of two or more objects. In a method, an object influence model is defined for a respective object with the object influence model having a shape and volume that encompasses the respective object. The object influence model extends beyond the respective object and varies in response to changes in one or more properties of the object. The method also includes receiving information from one or more sensors indicative of movement of the respective object and predicting its anticipated path of travel. The method further includes identifying a potential collision between the respective object and another object in an instance in which the object influence models of the respective object and another object intersect as the respective object is advanced along the anticipated path of travel.