Robot mapping method and device, robot, and storage medium
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Solution Overview
Problem
Traditional map-building methods for robots in unknown environments are time-consuming and laborious, especially in large and complex indoor spaces, as they often involve manual control and repeated scanning of the same regions.
Innovation Solution
A map-building method and device for robots that optimize the selection of target points by detecting exploration point information within a preset range, allowing the robot to travel to fewer positions and prevent repeated data collection, thereby shortening the map-building time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot continuously moves to the target point for map building, then the map building process can be completed, but the running time is prolonged due to repeated short-distance movements within the same region
Solution Approach 1:
The system performs preliminary detection of exploration point information within a preset range before the robot reaches the target point. By detecting whether other unexplored points exist in the vicinity ahead of time, the system can proactively adjust the target point selection, preventing the robot from making unnecessary movements to already-explored regions and thereby reducing overall map building time
Solution Approach 2:
The system implements a feedback mechanism where exploration point information is continuously detected and processed during robot movement. Based on the detected information about unexplored points within the preset range, the target point is dynamically adjusted. This closed-loop control ensures the robot always moves toward genuinely unexplored areas, eliminating redundant movements and improving map building efficiency
2Extent of automation
If the robot performs autonomous exploration without manual control, then human and material resources are saved, but the robot needs to make real-time decisions which increases system complexity
Solution Approach 1:
The autonomous exploration system is segmented into distinct functional modules: an exploration point detection module that identifies unexplored points, a target point selection module that chooses optimal destinations, and a navigation control module that executes movement. This modular segmentation simplifies the overall decision-making complexity by breaking it down into manageable, independent functions that can be processed sequentially
Solution Approach 2:
The robot performs self-service through autonomous detection and decision-making. The exploration point information detection and target point processing are automatically executed by the robot's own sensing and computing systems without external intervention. This self-service capability enables true autonomous exploration while keeping the control system relatively simple through rule-based decision logic
Data Source
AI summary
A map-building method for a robot is provided. The method includes: controlling the robot to travel toward a target point; processing the target point according to the exploration point information; and in response to that, the robot arrives at the target point, collecting map-building data of the region to be explored in order to update an environmental spatial map of the robot.