Obstacle recognition method for autonomous robots
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Solution Overview
Problem
Autonomous robots face challenges in efficiently navigating and mapping environments due to limitations in object recognition, path planning, and integration of sensor data, leading to suboptimal performance in tasks such as cleaning and mopping.
Innovation Solution
A method involving image sensors on robots to capture workspace images, process them to identify objects, and generate a planar representation of the environment, enabling the robot to execute actions based on object classification and movement data for efficient navigation and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data integration methods are used for environment mapping, then the robot can navigate autonomously, but the mapping precision and object recognition accuracy are insufficient
Solution Approach 1:
The patent combines multiple sensor types (image sensors, depth sensors, laser sensors) into a unified sensor fusion system that integrates their data streams to create a comprehensive environmental model. This merging of sensor data sources resolves the contradiction by achieving higher mapping precision through multi-sensor correlation while managing complexity through integrated processing architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives raw data from multiple sensors, performs synchronization and calibration, and generates standardized environmental representations. This intermediary system mediates between the complex sensor inputs and the navigation algorithms, improving mapping precision while abstracting the complexity of direct sensor integration.
2Reliability
If simple object recognition methods are used, then the robot can process data quickly, but the object classification accuracy is insufficient for effective path planning
Solution Approach 1:
The patent segments the object recognition process into multiple stages: initial detection using simple features, detailed classification using advanced image processing, and verification using contextual information. This segmentation allows the system to achieve high object recognition accuracy through progressive analysis while minimizing processing time by using simple methods for initial screening and reserving complex processing for ambiguous cases.
Solution Approach 2:
The patent applies partial processing to common objects with high confidence recognition, using only essential features for quick identification. For less common or ambiguous objects, the system applies excessive processing with multiple analysis methods to ensure accurate classification. This selective approach optimizes the balance between recognition accuracy and processing time.
3Productivity
If the robot uses basic navigation algorithms, then it can move autonomously, but the path planning efficiency is suboptimal for cleaning and mopping tasks
Solution Approach 1:
The patent implements dynamic path planning algorithms that continuously adapt to environmental changes and task requirements. The navigation system dynamically adjusts paths based on real-time obstacle detection, changing cleaning priorities, and energy constraints. This dynamic adaptation enables the robot to maintain high productivity in diverse environments while preserving the ability to handle various cleaning scenarios.
Solution Approach 2:
The patent changes key navigation parameters such as speed, turning radius, and cleaning coverage density based on the specific environment and task type. The system automatically adjusts these parameters to optimize productivity for different floor types, obstacle densities, and cleaning priorities, achieving high task completion efficiency while maintaining environmental adaptability through parameter optimization rather than algorithmic complexity.
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.