Adjustable Robot Vision for Predicted-Path Blind Spot Capture
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Solution Overview
Problem
Robots face challenges in navigating environments with unknown or uncertain environmental objects and attributes due to blind spots and limitations in vision systems, leading to reduced operational efficiency and increased collision risks.
Innovation Solution
The implementation of multiple vision components, including a fixed and an adjustable vision component, dynamically adjusted to mitigate unknown environmental objects and attributes, utilizing factors like planned paths, blind spots, and dynamic object locations to enhance data accuracy and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed vision component is used to capture vision data, then the device complexity is reduced, but the measurement precision and coverage of environmental objects are insufficient due to blind spots
Solution Approach 1:
The patent applies the dynamics principle by implementing an adjustable vision component that can dynamically change its pose relative to the robot frame. The adjustable vision component transitions between different poses to capture vision data from multiple angles, eliminating blind spots and improving environmental object detection accuracy without requiring a completely fixed vision system.
2Measurement precision
If the adjustable vision component is dynamically adjusted to multiple poses, then the measurement precision and coverage are improved, but the device complexity and operational complexity increase
Solution Approach 1:
The adjustable vision component serves multiple functions: it acts as both a fixed vision component when in a stable pose and as an adjustable vision component when transitioning between poses. This multi-functionality reduces the need for separate fixed and adjustable vision components, thereby managing device complexity while maintaining improved measurement precision.
3Reliability
If the adjustable vision component is dynamically adjusted during navigation, then the reliability of navigation is improved by reducing blind spots, but the productivity and navigation speed are reduced
Solution Approach 1:
The system performs preliminary action by capturing vision data at multiple poses before the robot completes its navigation task. The adjustable vision component anticipates potential blind spots along the navigation path and proactively captures data from multiple angles in advance, ensuring reliable navigation without requiring real-time adjustments that would slow down the robot.
4Measurement precision
If multiple vision components are used to cover blind spots, then the measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
Instead of using multiple fixed vision components simultaneously, the patent uses a single adjustable vision component that dynamically changes its pose to cover different areas. This approach achieves the same blind spot coverage as multiple fixed components but with fewer physical components, thereby reducing device complexity and cost while maintaining measurement precision.
Data Source
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AI summary
Implementations set forth herein relate to a robot that employs a stereo camera and LIDAR for generating point cloud data while the robot is traversing an area. The point cloud data can characterize spaces within the area as occupied, unoccupied, or uncategorized. For instance, an uncategorized space can refer to a point in three-dimensional (3D) space where occupancy of the space is unknown and/or where no observation has been made by the robot-such as in circumstances where a blind spot is located at or near a base of the robot. In order to efficiently traverse certain areas, the robot can estimate resource costs of either sweeping the stereo camera indiscriminately between spaces and/or specifically focusing the stereo camera on uncategorized space(s) during the route. Based on such resource cost estimations, the robot can adaptively maneuver the stereo camera during routes while also minimizing resource consumption by the robot.