Robot Camera Certification Using Light-Based Latency Checks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The use of uncertified cameras in autonomous mobile robots poses a performance gap due to glitches caused by latency, inadequate throughput, and minimalistic processing, which can lead to safety issues in navigation and obstacle avoidance, increasing costs when certified cameras are substituted to ensure safety.
Innovation Solution
An apparatus and method that includes a light source on the robot body to irradiate the camera's field of view, with a processing system to calculate latency and reactivity, ensuring the camera operates within safety-certified parameters by actuating the light source and monitoring the camera data, allowing continued operation or sending maintenance alerts if latency is not minimized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uncertified cameras are used in autonomous mobile robots, then cost is reduced, but safety and reliability deteriorate due to glitches caused by latency and inadequate throughput
Solution Approach 1:
The system continuously monitors camera performance metrics including latency, throughput, and reactivity in real-time. A processing system calculates these metrics by actuating a light source and monitoring camera data, providing feedback loops that detect when camera performance degrades below safety thresholds, enabling dynamic adjustment or shutdown to maintain safety
Solution Approach 2:
The system dynamically adjusts operational parameters of the camera system based on measured performance. By monitoring latency, throughput, and reactivity parameters and comparing them against safety thresholds, the system can adjust operating conditions or trigger maintenance alerts to ensure safety requirements are met while using cost-effective uncertified cameras
2Reliability
If certified safety camera systems are used, then safety and reliability are improved, but cost increases significantly
Solution Approach 1:
The system uses inexpensive uncertified cameras instead of expensive certified safety cameras, accepting that these cheaper components may have shorter operational lifetimes or require more frequent monitoring and maintenance. The real-time performance monitoring enables early detection of degradation, allowing maintenance before complete failure occurs
Solution Approach 2:
The system performs self-diagnosis and self-monitoring by continuously measuring camera performance metrics. The processing system automatically calculates latency, throughput, and reactivity without external intervention, enabling the robot to self-assess safety compliance and trigger maintenance alerts when needed
3Reliability
If real-time monitoring and calibration systems are added to uncertified cameras, then safety is improved, but device complexity increases
Solution Approach 1:
The monitoring and calibration functions are merged into the existing robot processing system rather than being implemented as separate external devices. The processing system that already controls the robot integrates the camera performance monitoring, latency calculation, and safety threshold comparison functions, reducing overall system complexity
Solution Approach 2:
The processing system performs multiple functions simultaneously: it controls robot navigation, processes sensor data, and monitors camera performance metrics. The light source serves dual purposes for both illumination and performance calibration, reducing the need for dedicated components
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 solution enables the use of non-certified cameras in safety-critical systems by providing real-time safety protocol calibration, ensuring the camera's performance meets safety needs, thereby reducing costs and enabling the use of autonomous robots in more contexts.
Implementation Method 1
at least one light source resident on the robot body proximate to the sensing camera such that the at least one light source is capable of at least partially irradiating a field of view (FoV) of the sensing camera
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus, system and method of for certifying a sensor that at least partially navigates an autonomous mobile robot. The apparatus may include at least a robot body; at least one light source resident on the robot body proximate to the sensing camera such that the at least one light source is capable of at least partially irradiating a field of view (FoV) of the sensing camera, wherein the at least one light source has characteristics substantially mated to the sensing camera; and at least one processing system that provides the at least partial navigation. The at least one processing system may execute the steps of: actuating the at least one light source at a predetermined time and for a predetermined duration; monitoring data from the sensing camera for confirmation of the actuating; calculating at least one of the latency, throughput, and reactivity of the sensing camera based on the monitoring; and at least partially navigating based on the calculating.