Robot Obstacle Recognition Using Object Dictionaries to Avoid Entanglement
Find Innovative SolutionsGenerate Solutions
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 capture workspace images, identify object characteristics using an object dictionary, and adjust navigation paths to avoid recognized objects through deep learning and preprogrammed instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots operate without object recognition capabilities, then device complexity is reduced, but reliability deteriorates due to entanglement with objects like cords and wires
Solution Approach 1:
The robot performs preliminary actions by capturing images of the workspace before movement, identifying objects and their characteristics in advance, and planning navigation paths that avoid recognized objects. This preliminary object recognition and path planning prevents entanglement with cords, wires, and other objects before they become problematic obstacles during operation.
2Productivity
If the robot uses simple navigation without object identification, then device complexity is reduced, but productivity deteriorates due to task interruption from object entanglement
Solution Approach 1:
The robot employs feedback mechanisms by continuously capturing images of the workspace, comparing detected objects against an object dictionary to identify object types, and adjusting its navigation path based on this recognition feedback. This closed-loop system allows the robot to adapt its movement in real-time to avoid objects, ensuring continuous productive operation without task interruptions from entanglement.
3Reliability
If the robot employs comprehensive object recognition and avoidance, then reliability is improved, but ease of operation deteriorates due to complex navigation adjustments
Solution Approach 1:
The robot performs self-service by autonomously capturing images, identifying objects using an object dictionary, determining object types, and adjusting its own navigation path without external intervention. The system independently handles the complex tasks of object recognition and path replanning, freeing operators from the burden of manual navigation control while maintaining high reliability through continuous adaptive avoidance of recognized objects.
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.