Adjustable Robot Vision for Blind-Spot Mapping Along Predicted Paths
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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 components, leading to reduced operational efficiency and increased collision risks.
Innovation Solution
Implementing a system with multiple vision components, including a fixed and adjustable vision component, where the adjustable component is dynamically positioned to mitigate blind spots and improve accuracy in object detection, using factors like planned paths, blind spots, and dynamic object locations to enhance vision data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed vision component is used to capture vision data, then the device complexity is reduced, but blind spots are created leading to reduced measurement precision
Solution Approach 1:
The patent applies the dynamics principle by making the vision component adjustable rather than fixed. The vision component can be dynamically repositioned to different locations and orientations to capture images from multiple perspectives, eliminating blind spots and improving object detection accuracy without requiring multiple fixed components throughout the environment.
Solution Approach 2:
The patent uses a computing device as an intermediary that processes images captured from different positions and synthesizes them into a comprehensive environmental representation. This intermediary system allows a single adjustable vision component to achieve the效果 of multiple fixed components by intelligently combining data from various viewpoints.
2Reliability
If the robot reduces velocity to account for unknown environmental conditions, then collision risk is reduced, but productivity decreases
Solution Approach 1:
The patent applies preliminary action by capturing images and identifying environmental objects and conditions before the robot reaches them. The system proactively builds a comprehensive understanding of the environment ahead of time, allowing the robot to maintain higher speeds while still safely navigating unknown areas since potential hazards are already identified and accounted for.
3Measurement precision
If multiple vision components are deployed to eliminate blind spots, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent resolves this contradiction by using a single adjustable vision component that can be dynamically repositioned to capture images from multiple locations. This dynamic approach achieves comprehensive environmental coverage equivalent to multiple fixed components while maintaining simpler device architecture with only one vision component.
Solution Approach 2:
The adjustable vision component serves multiple functions by capturing images from various positions and angles. This single component performs the role of what would otherwise require multiple specialized fixed components, achieving universal coverage while reducing overall system complexity.
Data Source
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.