Autonomous Material Vehicles With Sensor-Based Controlled Zone Safing
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Solution Overview
Problem
There is a need to maintain safety in controlled zones within industrial facilities by preventing robotic equipment from entering or operating within these zones without relying solely on human discretion, especially with the introduction of self-driving material-transport vehicles.
Innovation Solution
The method involves moving robotic equipment towards a controlled zone, capturing environmental data, comparing it with known-good data to determine environmental changes, and operating in a safe mode, which may include limiting speed or preventing movement of the equipment and its manipulator arm, using sensors like LiDAR and cameras to ensure safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human discretion is used to prevent entry into controlled zones, then safety can be maintained, but the system relies on human judgment which may be insufficient with autonomous vehicles
Solution Approach 1:
The system continuously captures environmental data using sensors (cameras, LiDAR, thermal sensors) and compares it with known-good data to detect changes. This feedback loop enables the autonomous vehicle to automatically respond to safety conditions without human intervention, resolving the contradiction by providing reliable automated safety monitoring that scales with automation levels.
Solution Approach 2:
The patent replaces human discretion (mechanical/system-based human judgment) with automated sensor systems and computational algorithms. Sensors capture environmental data, processors analyze changes, and the system automatically adjusts operation modes, substituting human cognitive functions with automated detection and decision-making systems suitable for autonomous vehicles.
2Reliability
If environmental monitoring is continuously performed using sensors, then safety can be automatically detected, but the device complexity increases
Solution Approach 1:
The system uses multi-functional sensors (cameras, LiDAR, thermal sensors) that can detect various environmental conditions simultaneously. These sensors serve multiple purposes: detecting unauthorized entry, monitoring environmental changes, and tracking vehicle position, thereby achieving comprehensive safety detection without proportionally increasing system complexity.
Solution Approach 2:
The system compares captured environmental data with pre-stored known-good data to automatically detect changes and determine safety status. This self-service approach enables the system to autonomously monitor its environment and make safety decisions without requiring complex external monitoring infrastructure, reducing overall system complexity while maintaining reliable safety detection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances safety by automatically determining the safety status of controlled zones and preventing unauthorized entry or operation of robotic equipment, thereby reducing the risk of human-robot interaction hazards.
Implementation Method 1
at least one sensor comprises a LiDAR device and the captured environmental data comprise LiDAR scan data
Implementation Method 2
the at least one sensor comprises a camera and the captured environmental data comprise an image
Implementation Method 3
the at least one sensor comprises a thermal sensor and the captured environmental data comprise a temperature
Data Source
AI summary
Systems and methods for operating robotic equipment in a controlled zone are presented. The system comprises one or more self-driving material-transport vehicles having at least one sensor, non-transitory computer-readable media, and a processor in communication with the at least one sensor and media. The media stores computer instructions that configure the processor to move the vehicle towards the controlled zone in a normal mode of operation, capture environmental data associated with the controlled zone using the at least one sensor, determine environmental-change data based on comparing the captured environmental data with known-good environmental data, and operating the vehicle in a safe mode of operation based on the environmental-change data.