Proactive Safety Systems for Robotics Using External Sensor Fusion
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Solution Overview
Problem
Conventional safety measures in industries like factory automation and autonomous driving are reactive, failing to detect potential safety events proactively, leading to production losses and limited coverage due to reliance on internal sensors with restricted fields of view.
Innovation Solution
Implementing proactive safety systems that use external sensors like cameras, LiDAR, and RADAR to monitor environments and process data with machine learning models to detect potential safety events, activating warnings or safety measures before incidents occur, thereby increasing safety and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive safety functions are implemented to mitigate safety events, then safety response capability is improved, but production downtime increases due to manual restart requirements
Solution Approach 1:
The system performs preliminary actions by proactively detecting potential safety events before they occur and automatically executing safety measures in advance. This eliminates the need for manual intervention and restart operations after safety events, thereby maintaining safety response capability while preventing production downtime.
2Reliability
If internal safety sensors are installed on equipment, then safety detection function is provided, but monitoring coverage area is limited due to restricted fields of view
Solution Approach 1:
The system introduces external sensors positioned at strategic locations around the equipment as intermediaries to expand monitoring coverage. These external sensors capture environmental data that internal sensors cannot detect, such as workers approaching from blind spots or objects in surrounding areas, thereby maintaining safety detection function while significantly increasing monitored coverage area.
3Area of stationary object
If external sensors are used to expand monitoring coverage, then environmental coverage area is increased, but system complexity increases
Solution Approach 1:
The system applies multi-functionality by using a centralized processing unit that handles data from both internal and external sensors, performs machine learning-based safety event detection, and coordinates automatic safety measure execution. This universal approach allows the system to manage expanded monitoring coverage from multiple sensors without proportionally increasing overall system complexity.
Data Source
AI summary
In various examples, providing proactive safety measures for robotic systems and equipment is described herein. For instance, systems and methods may monitor equipment (e.g., a machine) in order to proactively detect potential safety events that may occur with regard to the equipment and/or activate one or more proactive safety measures in order to mitigate and/or eliminate the safety events before occurring. For example, sensors that are external to the equipment may generate sensor data representing the environment at least partially surrounding the equipment. The sensor data may then be processed to determine when a potential safety event is occurring with regard to the equipment. One or more safety measures may then be activated based at least on detecting the potential safety event, such as activating a warning and/or causing an internal reactive safety measure of the equipment to activate.