Obstacle recognition method for autonomous robots
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Solution Overview
Problem
Autonomous robots face challenges in efficiently navigating and performing tasks within environments due to limitations in mapping, localization, and object recognition technologies, which hinder their ability to operate autonomously with minimal human intervention.
Innovation Solution
A method involving image capture by a robot's camera, processing of images to compare objects with an object dictionary, and using an object classification unit to identify object classes and instruct actions based on those classifications, along with spatial alignment of images to correct movement data and generate a spatial representation of the workspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous robots use traditional mapping and object recognition methods, then they can operate with some level of autonomy, but their navigation and task performance are hindered by limitations in recognition accuracy and adaptability
Solution Approach 1:
The system dynamically adjusts its operation by switching between different modes (exploration mode for mapping new environments, operation mode for task execution, recovery mode for obstacle avoidance). The robot continuously updates its spatial representation and adapts its navigation strategy based on real-time sensor data and identified objects, making the system flexible and responsive to changing environmental conditions
Solution Approach 2:
The system changes operational parameters based on environment type. For example, it adjusts navigation speed, sensor sampling rates, and path planning parameters depending on whether the environment is familiar or new, the density of obstacles, and the confidence level of object recognition. This allows optimal performance across diverse conditions
2Measurement precision
If robots capture and process multiple images from different locations for spatial representation, then navigation accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary spatial alignment by detecting features in images and establishing correspondence between features from different locations before full image processing. This preliminary feature-based alignment provides a rough spatial framework that guides subsequent more detailed processing, reducing overall computation time while maintaining accuracy
Solution Approach 2:
The image processing is segmented into distinct stages: feature detection, spatial alignment, object recognition, and path planning. Each stage processes only the necessary data for its specific function, avoiding redundant computation. The system processes images from multiple locations but only performs full analysis on regions containing identified objects or obstacles
3Productivity
If robots use complex object classification and recognition systems, then task performance in complex environments improves, but device complexity increases
Solution Approach 1:
The system uses a universal object classification unit that handles multiple object types (obstacles, furniture, tools, personal items) with a single integrated approach. This unit processes images, compares them against an object dictionary, and identifies object classes, which then triggers appropriate pre-programmed actions. This multi-functional approach improves task performance without proportionally increasing complexity
Solution Approach 2:
The robot autonomously identifies objects, classifies them, and selects appropriate actions without human intervention. The system self-manages its navigation, obstacle avoidance, and task execution based on its own sensor data and internal models, reducing the need for complex external control systems
Data Source
AI summary
Provided is a method including capturing, by an image sensor disposed on a robot, images of a workspace; obtaining, by a processor of the robot or via the cloud, the captured images; comparing, by the processor of the robot or via the cloud, at least one object from the captured images to objects in an object dictionary; identifying, by the processor of the robot or via the cloud, a class to which the at least one object belongs using an object classification unit; and instructing, by the processor of the robot, the robot to execute at least one action based on the object class identified.