Autonomous Robot Obstacle Recognition Using Multi-View Object Mapping
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Solution Overview
Problem
Autonomous robots face challenges in efficiently navigating and mapping environments due to limitations in object recognition and path planning, leading to potential collisions and incomplete mapping.
Innovation Solution
A method involving image sensors and processors that capture and analyze workspace images, compare objects to an object dictionary, and generate a planar representation of the environment for navigation, allowing the robot to identify objects and adjust its path accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle recognition methods are used, then the robot can identify some objects, but the recognition accuracy is insufficient leading to collisions
Solution Approach 1:
The patent combines multiple image sensors (front, rear, left, right) to capture images from different directions simultaneously. The processor integrates these multiple images to comprehensively identify obstacles, including transparent objects that single-sensor systems miss. This merging of sensory inputs resolves the contradiction by improving recognition accuracy without compromising navigation reliability.
Solution Approach 2:
The patent introduces an object dictionary as an intermediary reference system. Captured images are compared against stored object templates in the dictionary to accurately identify unknown objects. This intermediary reference mechanism enables reliable recognition of diverse objects including transparent materials, resolving the accuracy-reliability contradiction by providing a systematic identification framework.
2Measurement precision
If comprehensive environment mapping is performed, then the robot creates accurate workspace maps, but the mapping time and processing complexity increase
Solution Approach 1:
The patent pre-stores object templates and characteristics in an object dictionary before actual mapping operations. During mapping, the system performs rapid template matching rather than full image analysis. This preliminary preparation reduces real-time processing complexity while maintaining high mapping accuracy, resolving the time-accuracy contradiction.
Solution Approach 2:
The patent divides the workspace mapping into multiple sequential image captures from different robot positions and orientations. Each image is processed independently using template matching, then integrated into the complete workspace map. This segmentation approach reduces the computational burden of processing large panoramic images while maintaining comprehensive mapping accuracy.
3Speed
If the robot uses simple navigation methods, then the processing speed is fast, but the robot experiences collisions with obstacles
Solution Approach 1:
The patent implements continuous feedback by capturing images before, during, and after navigation movements. The processor constantly compares current images against the object dictionary and previously mapped areas, providing real-time feedback on obstacle positions. This enables the robot to maintain high navigation speed while reliably avoiding collisions through continuous environmental monitoring and adjustment.
Solution Approach 2:
The patent employs dynamic obstacle detection by capturing images at multiple robot positions and orientations during movement. The system adaptively adjusts navigation based on detected transparent objects and dynamic obstacles. This dynamic approach allows fast navigation while maintaining collision avoidance reliability through continuous environmental assessment.
Data Source
AI summary
Provided is a method for operating a robot, including capturing images of a workspace, comparing at least one object from the captured images to objects in an object dictionary, identifying a class to which the at least one object belongs using an object classification unit, instructing the robot to execute at least one action based on the object class identified, capturing movement data of the robot, and generating a planar representation of the workspace based on the captured images and the movement data, wherein the captured images indicate a position of the robot relative to objects within the workspace and the movement data indicates movement of the robot.