Robot Assembly Path Adjustment Using Tags and Human Posture Nodes
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Solution Overview
Problem
Conventional safety measures for robots in manufacturing environments often compromise productivity and fail to prevent collisions with humans, especially when humans move unpredictably, due to their restrictive nature and inability to adapt to dynamic environments.
Innovation Solution
A robot assembly equipped with a positional apparatus having tags and a detector system that generates a movement model, allowing it to detect humans and adjust its movements to avoid collisions by capturing images, extrapolating 3D information, and generating posture nodes to predict human motion, thereby modifying its path to ensure safety without hindering productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots perform tasks quickly to maintain productivity, then productivity is improved, but the risk of collision and harm to humans increases
Solution Approach 1:
The system performs preliminary actions by continuously capturing images and generating movement models before collisions occur. The vision system proactively identifies human presence and predicts potential collision scenarios, allowing the robot to adjust its path in advance rather than reacting after a collision risk materializes.
Solution Approach 2:
The system implements continuous feedback by capturing images during robot operation, processing them to detect humans, and using this information to dynamically adjust robot movement. The feedback loop includes capturing images, generating movement models, detecting human presence, and modifying robot behavior in real-time to prevent collisions while maintaining productivity.
2Object-affected harmful factors
If physical barriers are installed around robot operating areas to ensure safety, then safety is improved, but the available workspace and productivity are reduced
Solution Approach 1:
The system replaces mechanical safety barriers with a vision-based detection and control system. Instead of using physical structures to prevent collisions, the system uses image capture, processing, and real-time robot control to achieve the same safety outcome while maintaining full workspace accessibility.
Solution Approach 2:
The vision system acts as an intermediary between the robot and the human environment. Rather than physically separating them with barriers, the intermediary system continuously monitors the environment, detects humans, and mediates robot behavior to ensure safe operation in shared spaces.
3Object-affected harmful factors
If safety monitoring systems are implemented to detect humans and adjust robot movement, then collision prevention is improved, but system complexity increases
Solution Approach 1:
The vision system performs multiple functions using a single integrated approach: it captures images for both movement model generation and human detection, processes visual information for both robot navigation and safety monitoring, and serves both productivity optimization and collision prevention goals simultaneously.
Solution Approach 2:
The system merges the movement model generation process with human detection and safety monitoring into a unified vision processing framework. The same image capture and processing infrastructure used for robot navigation is also used for detecting humans and preventing collisions, reducing overall system complexity.
Data Source
AI summary
A robot assembly for safe operation in a manufacturing setting with humans including a sensor for detecting movement of the robot assembly and a human location. A positional apparatus including at least one tag located on the robot apparatus and at least one detector for detecting the tag. Posture nodes associated with a human saved in memory. A method that includes, generating a task movement plan based on images captured by the sensors, tags detected by the detector, and posture nodes of a nearby human captured by the sensor.