Autonomous Robot Object Recognition for Entanglement Avoidance
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Solution Overview
Problem
Autonomous robots often malfunction or fail to complete tasks due to encountering objects like cords, wires, and toys that get stuck in their wheels or moving parts, necessitating a method to avoid entanglement and obstruction.
Innovation Solution
Equipping autonomous robots with image sensors and processors that use deep learning to identify objects through an object dictionary, allowing the robot to alter its navigation path and avoid collisions by recognizing predefined obstacles such as cables, cords, and other objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot operates autonomously without object recognition, then the device complexity is reduced, but the robot encounters objects like cords and toys that cause malfunction and task failure
Solution Approach 1:
The robot captures images of the workspace and identifies objects before moving into the workspace, allowing it to plan its path and avoid objects like cords and toys that would cause malfunction. This preliminary detection and identification action prevents entanglement issues before they occur.
Solution Approach 2:
An image sensor and image processing system serve as an intermediary between the robot and physical objects. The system captures visual information, processes it through an object dictionary, and provides object location data that enables the robot to navigate around obstacles without direct physical interaction.
2Reliability
If the robot uses image sensors and deep learning to identify objects, then the robot can avoid entanglement with objects, but the device complexity increases
Solution Approach 1:
The image sensor and processing system serve multiple functions: capturing images of the workspace, identifying various types of objects (cords, toys, clothing), determining object locations, and providing navigation data. This multi-functional approach consolidates what could be multiple separate systems into a unified object recognition platform.
Solution Approach 2:
The system uses an object dictionary that contains characteristic data representing different object types. Instead of analyzing every pixel of every object in real-time, the system compares image features against pre-stored object characteristics, using a simplified representation (copy) of object properties for rapid identification.
3Reliability
If the robot captures and processes images to identify objects, then the robot can recognize and avoid obstacles, but the loss of time for image processing occurs
Solution Approach 1:
The robot captures images of the workspace before beginning its cleaning operation and processes these images to identify objects and plan its navigation path in advance. This preliminary image capture and processing allows the robot to avoid objects during operation without continuous real-time processing delays.
Solution Approach 2:
The system identifies at least one characteristic of objects rather than analyzing all possible features of every object in the workspace. This partial analysis approach focuses computational resources on key identifying features, reducing processing time while maintaining sufficient object recognition accuracy for navigation purposes.
Data Source
AI summary
Provided is a robot, including: a plurality of sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with an image sensor, images of a workspace as the robot moves within the workspace; identifying, with the processor, at least one characteristic of at least one object captured in the images of the workspace; determining, with the processor, an object type of the at least one object based on characteristics of different types of objects stored in an object dictionary; and instructing, with the processor, the robot to execute at least one action based on the object type of the at least one object.