Adaptive Illumination Control for Human-Aware Data Capture
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Solution Overview
Problem
In dynamic environments like retail facilities, mobile automation apparatuses face challenges in data capture due to interference from illumination, which can affect customers and staff, complicating the dynamic nature of these settings.
Innovation Solution
A mobile automation apparatus equipped with image sensors, depth sensors, and an illumination subsystem, where a navigational controller obtains image and depth data, detects obstacles, classifies them as human or non-human, and adjusts illumination accordingly to avoid interference with humans, using methods like obstacle detection and feature recognition to control the illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If illumination is increased to improve data capture quality, then data capture efficiency is improved, but interference with customers and staff increases
Solution Approach 1:
The illumination subsystem dynamically adjusts its operation based on real-time environmental conditions. The navigational controller continuously monitors sensor data including camera images and depth sensor readings to detect human presence, then adapts illumination behavior accordingly - using high intensity when no humans are present and reducing or directing illumination when humans are detected, thus resolving the contradiction between data capture efficiency and human comfort
Solution Approach 2:
The system applies illumination selectively to specific regions rather than uniformly across the entire field of view. By using depth sensors and camera data to identify human locations, the illumination subsystem directs light only to areas requiring data capture while avoiding regions where humans are present, thereby maintaining data capture quality without causing harmful illumination interference to people
2Object-affected harmful factors
If illumination is reduced to minimize interference with humans, then harmful factors to humans are reduced, but data capture quality deteriorates
Solution Approach 1:
The system dynamically switches between different illumination modes based on detected human presence and distance. When humans are detected, the navigational controller adjusts illumination intensity and direction in real-time, maintaining sufficient lighting for data capture in regions without humans while minimizing illumination in human-occupied areas, thus preventing deterioration of overall data capture quality
Solution Approach 2:
The field of view is segmented into multiple regions based on human detection data from cameras and depth sensors. The illumination subsystem then applies different illumination levels to different segments - high illumination to segments requiring data capture and low or zero illumination to segments containing humans - thereby maintaining measurement precision in critical areas while reducing harmful effects in others
3Measurement precision
If obstacle detection and classification systems are added to control illumination, then illumination control precision is improved, but device complexity increases
Solution Approach 1:
The system employs multi-functional sensors that serve multiple purposes. The camera and depth sensor are used both for navigation and obstacle avoidance, as well as for human detection and classification. This multi-functionality allows the system to achieve precise illumination control based on obstacle detection without adding dedicated specialized sensors, thereby limiting the increase in device complexity
Solution Approach 2:
The obstacle detection, human classification, and illumination control functions are merged into a unified system managed by the navigational controller. Rather than separate independent systems, the same sensor data processing pipeline that identifies obstacles for navigation is also used to detect humans for illumination control, consolidating functionality and reducing overall system complexity while maintaining precision
Data Source
AI summary
A method in a navigational controller includes: obtaining image data and depth data from corresponding sensors of a mobile automation apparatus; detecting an obstacle from the depth data and classifying the obstacle as one of a human obstacle and a non-human obstacle; in response to the classifying of the obstacle as the human obstacle, selecting a portion of the image data that corresponds to the obstacle; detecting, from the selected image data, a feature of the obstacle; based on a detected position of the detected feature, selecting an illumination control action; and controlling an illumination subsystem of the mobile automation apparatus according to the selected illumination control action.