Spatial Group Monitoring for Collision-Aware Object Trajectory Control
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Solution Overview
Problem
Manufacturing environments with automated processes lack effective oversight to prevent collisions and damage between objects moving on conveyor belts or in unpredictable physical environments, leading to equipment damage and potential shutdowns.
Innovation Solution
A computer-program product that uses image data to detect and track objects, simulates their movement, and generates autonomous indications to augment the physical environment, preventing collisions by predicting and adjusting object trajectories based on predefined objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processes are used without direct operator oversight, then productivity increases, but reliability deteriorates due to undetected collisions and equipment damage
Solution Approach 1:
The system enables automated processes to monitor and protect themselves through computer vision technology. Objects are tracked autonomously without human oversight, with the system automatically detecting collisions, predicting trajectories, and generating warnings to prevent equipment damage, allowing continuous operation while maintaining reliability
Solution Approach 2:
Manual operator oversight is replaced with an automated computer vision system using cameras, processors, and algorithms. The mechanical monitoring function previously performed by human operators is substituted with electronic image processing, trajectory prediction, and automated warning generation, maintaining productivity while improving reliability through consistent automated surveillance
2Measurement precision
If physical environments with unpredictable object movement are monitored manually, then measurement precision may be maintained, but productivity deteriorates due to difficulty in tracking and controlling multiple objects
Solution Approach 1:
Manual tracking and control of multiple unpredictable objects is replaced with an automated computer vision system. The system uses image processing algorithms to automatically detect, track, and predict the movement of multiple objects simultaneously, maintaining measurement precision while dramatically improving productivity by eliminating the need for manual monitoring of complex environments
Data Source
AI summary
A computing system obtains image data representing images. Each of the images is captured at different time points of a physical environment. The physical environment comprises a first object and a second object. The computing system executes a control system to augment the physical environment. The control system detects a group forming in the images. The control system tracks an aspect of a movement, of a given object, in the group. The control system simulates the physical environment and the movement, of the given object, in the group in a simulated environment. The control system evaluates simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group. The control system generates based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment.