360° Depth Camera Obstacle Detection for Tall Mobile Robots
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Solution Overview
Problem
Autonomous mobile robots face navigation challenges due to obstacles at various altitudes and unstructured environments, particularly when operating around untrained personnel or the general public, as existing sensors provide inadequate obstacle detection and handling, leading to safety issues and inefficient path planning.
Innovation Solution
Employing at least two three-dimensional depth camera sensors affixed to the robot body for a 360-degree field of view, combined with a processing system that analyzes pixel data to distinguish between obstacles, shadows, and the robot's body, using templates to filter out missing or erroneous pixels for accurate obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle sensors are used on autonomous robots, then the robot can detect some obstacles, but the detection accuracy is insufficient and cannot distinguish between obstacles, shadows, and the robot's body
Solution Approach 1:
The patent segments the detection task by using multiple depth camera sensors positioned at different locations (front, rear, left, right) to capture depth information from different perspectives. This segmentation allows the system to analyze pixel data from multiple viewpoints and distinguish between actual obstacles, shadows, and the robot's body by comparing depth measurements across sensors.
Solution Approach 2:
The patent introduces template comparison as an intermediary mechanism. Depth pixel data from sensors is compared against pre-established templates that represent the robot's body and shadow patterns. This template-mediated comparison enables the system to filter out false positives (shadows mistaken for obstacles) and accurately identify real obstacles by matching depth signatures.
2Adaptability or versatility
If the robot operates in unstructured environments around untrained personnel, then the robot gains operational versatility, but safety and obstacle handling become significantly more difficult
Solution Approach 1:
The patent changes the detection parameters by using depth information (z-axis measurement) in addition to x-y plane imaging. Depth camera sensors provide quantitative depth values that allow the system to set dynamic detection thresholds and parameters based on the robot's height, sensor positions, and environmental conditions. This parameter adaptation enables reliable obstacle detection whether personnel are present or absent, improving safety while maintaining operational versatility.
3Adaptability or versatility
If the robot is a meter or more in height, then the robot can operate in more diverse indoor environments, but the distance between obstacle sensors and the floor increases, creating navigation difficulties
Solution Approach 1:
The patent addresses the height challenge by transitioning from two-dimensional image analysis to three-dimensional depth analysis. Depth camera sensors capture z-axis information, allowing the system to create a 3D understanding of the environment. This dimensional change enables the robot to accurately perceive obstacles regardless of its height above the floor, as the depth measurements directly encode vertical distance information.
Solution Approach 2:
The patent segments the robot's body into multiple detection zones by positioning sensors at different heights and locations. The template comparison method creates separate depth signatures for different parts of the robot body and its shadow. This segmentation allows the system to handle the increased sensor-to-floor distance by analyzing depth information from multiple segmented perspectives rather than relying on a single sensor position.
Data Source
AI summary
An apparatus, system and method of operating an autonomous mobile robot having a height of at least one meter. The robot body; at least two three-dimensional depth camera sensors affixed to the robot body proximate to the height, wherein the sensors are directed toward a floor surface and, in combination, comprise a substantially 360 degree field of view of the floor surface around the robot body; and a processing system for receiving pixel data within the field of view of the sensors; obtaining missing or erroneous pixels from the pixel data; comparing the missing or erroneous pixels to a template, wherein the template comprises at least an indication of ones of the missing or erroneous pixels indicative of the robot body and a shadow of the robot body; and outputting an indication of obstacles in or near the field of view based on the comparing.