Obstacle recognition method and apparatus, medium and electronic device
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Walking robots, such as sweeping robots, have limited ability to identify obstacles like pet feces, often misidentifying or failing to identify them, leading to unsatisfactory cleaning results.
Innovation Solution
The method involves acquiring current identification feature information, bypassing to multiple positions around the obstacle to collect additional information, and determining the obstacle type based on comprehensive analysis of all collected data, including environment maps and confidence thresholds to enhance identification reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses a camera to identify obstacles, then the identification process is simple and fast, but the identification accuracy is low and misidentification occurs frequently
Solution Approach 1:
The patent transitions from single-position camera identification to multi-position identification by moving the robot to different locations around the obstacle. This dimensional change in observation position enables the system to capture diverse visual features and environmental context, significantly improving identification accuracy without requiring complex additional sensors
Solution Approach 2:
The system performs preliminary identification at the current position before determining whether multi-position identification is needed. This staged approach allows the robot to quickly handle obvious cases while only applying the more complex multi-position process when necessary, balancing accuracy with operational efficiency
2Productivity
If the robot bypasses obstacles quickly without detailed identification, then cleaning efficiency is high, but cleaning completeness deteriorates due to misidentification
Solution Approach 1:
The system uses environmental maps and historical identification data as feedback to guide the identification process. By comparing current observations with stored environmental information, the robot can verify obstacle types and adjust its cleaning behavior, ensuring both efficiency and completeness
Solution Approach 2:
The identification strategy dynamically adapts based on confidence levels and environmental context. When identification confidence is high, the system proceeds quickly; when confidence is low or obstacles are ambiguous, the system automatically triggers multi-position identification, creating a dynamic balance between speed and accuracy
Data Source
AI summary
A method for identifying an obstacle, including: acquiring current identification feature information of the obstacle; bypassing to a position around the obstacle in response to determining that the current identification feature information does not satisfy an identification condition, and acquiring identification feature information of the obstacle at the position correspondingly; and determining a target type of the obstacle based on all identification feature information of the obstacle.