Autonomous Robot Obstacle Recognition Using Multi-Sensor Integration
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Solution Overview
Problem
Existing autonomous robotic systems face challenges in efficiently mapping and navigating complex environments, particularly in discovering new areas and avoiding obstacles, due to limitations in sensor data integration and processing.
Innovation Solution
The proposed solution involves a method for operating a robot that includes capturing images with an image sensor, tracking wheel rotations, and using LIDAR to generate and update a map of the workspace. The robot discriminates between objects and the floor surface, adjusts its path accordingly, and continues to map until all areas are discovered and included in the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses multiple sensors (image sensor, LIDAR, wheel rotation tracking) to improve obstacle detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the sensing system into separate functional modules: image sensor for visual obstacle recognition, LIDAR for depth and distance measurement, and wheel rotation sensors for position tracking. Each sensor type processes specific aspects of the environment independently, then the processor integrates these segmented data streams to achieve comprehensive obstacle detection with high precision while managing system complexity through modular architecture
Solution Approach 2:
The processor acts as an intermediary that receives raw data from multiple sensors, processes and correlates the information, and generates unified obstacle detection results. This intermediary processing layer harmonizes the data from different sensor types, enabling accurate obstacle identification without requiring direct complex integration between all sensor components
2Adaptability or versatility
If the robot continuously updates the map as it moves into new areas, then adaptability improves, but use of energy increases
Solution Approach 1:
The robot performs map updates at periodic intervals or at specific trigger points during navigation, such as when entering newly discovered areas or after completing predefined exploration segments. This periodic mapping approach maintains adaptability by regularly incorporating new environmental information while reducing energy consumption by avoiding continuous real-time map regeneration throughout the entire operation
Solution Approach 2:
The robot performs preliminary exploration and mapping of new areas before executing detailed cleaning tasks. By预先 (in advance) discovering and mapping the workspace boundaries and major features, the system reduces subsequent processing energy requirements during actual cleaning operations, as the foundational map structure is already established
3Measurement precision
If the robot uses LIDAR to generate detailed maps of the workspace, then measurement precision improves, but use of energy increases
Solution Approach 1:
The LIDAR system performs measurements at selective key positions and angles during robot navigation, capturing essential spatial information for accurate mapping without continuously scanning all areas. The system applies partial action by focusing LIDAR measurements on critical boundaries, obstacles, and navigation-relevant features rather than exhaustive comprehensive scanning, thereby achieving sufficient mapping precision with reduced energy expenditure
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the robot to efficiently create and update maps of its environment, allowing for effective navigation and task execution, such as cleaning, by accurately distinguishing between objects and the floor surface.
Implementation Method 1
capturing, by a LIDAR disposed on the robot, LIDAR data as the robot moves within the workspace, wherein the LIDAR data is indicative of distances from a position of the LIDAR to objects and perimeters surrounding the robot
Implementation Method 2
capturing, by at least one image sensor disposed on the robot, images of a workspace
Data Source
AI summary
A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.