Robot Frontier Exploration for Faster Unknown Region Mapping
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Solution Overview
Problem
Current map building methods for robots are inefficient in rapidly acquiring maps of unknown regions, affecting navigation efficiency and not considering the accessibility of the map building region.
Innovation Solution
An RRT algorithm-based map exploration method that identifies frontier points meeting preset passing conditions, checks exploration repeatability, and selects the highest revenue point for navigation, guiding the robot to build a map within the unknown region using a chip and sensor-equipped robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If map building is performed gradually during the sweeping process, then the robot can build a map continuously, but the robot cannot rapidly acquire a map of an unknown region
Solution Approach 1:
The patent performs preliminary actions by pre-processing the environment model to identify frontier points and passable conditions before actual exploration. The RRT algorithm pre-generates exploration paths to multiple frontier points, and the system pre-calculates navigation costs and income information, enabling rapid response when exploration is needed without performing these computations in real-time during the sweeping process.
Solution Approach 2:
The patent segments the unknown region into multiple discrete frontier points that can be independently evaluated and explored. Each frontier point represents a distinct exploration target with its own navigation cost and income information, allowing the robot to selectively explore high-value areas rather than performing exhaustive sweeping coverage, thus accelerating map acquisition of unknown regions.
2Reliability
If all frontier points are explored without filtering, then comprehensive map coverage is achieved, but calculation costs and time consumption increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between different types of frontier points based on their exploration value. The filter module evaluates each frontier point's passable condition, navigation cost, and income information to assign different exploration priorities. High-value frontier points with good passability and low navigation costs are explored first, while low-value points are deferred or skipped, optimizing the balance between map completeness and calculation efficiency.
Solution Approach 2:
The patent changes parameters by introducing a filtering mechanism that dynamically adjusts which frontier points are selected for exploration based on multiple parameters including passable condition, navigation cost, and income information. This parameter-based selection transforms the exploration process from exhaustive to selective, reducing calculation load while maintaining reliable map coverage by focusing computational resources on the most valuable exploration targets.
3Reliability
If the robot explores all regions systematically, then complete map coverage is ensured, but navigation efficiency and exploration speed decrease
Solution Approach 1:
The patent performs preliminary evaluation of all frontier points to calculate navigation costs and assess passable conditions before actual exploration begins. This pre-processing allows the robot to identify and prioritize high-value exploration targets, enabling efficient navigation to the most important unknown regions first while ensuring that less valuable areas can be covered later if needed, thus improving overall exploration speed without sacrificing eventual map completeness.
Solution Approach 2:
The patent implements feedback by continuously evaluating frontier points based on navigation costs, income information, and passable conditions. The filter module provides feedback on which frontier points meet the exploration criteria, allowing the robot to adapt its navigation strategy in real-time. This feedback mechanism ensures that the robot systematically covers high-value regions while maintaining the capability to achieve complete map coverage, balancing exploration efficiency with reliability.
Data Source
AI summary
Disclosed are a map exploration method for exploring an unknown region by a robot, a chip, and the robot. The map exploration method includes: step S1: acquiring frontier points that meet a preset passing condition by means of a frontier detector based on a rapid exploration random tree algorithm; step S2: filtering out frontier points for exploring the unknown region among the frontier points acquired in step S1; step S3: on the basis of navigation costs of frontier points explored by the robot at a current position and income information corresponding to the navigation costs, and by considering a passable condition of the frontier points, selecting a frontier point with the highest revenue from the frontier points filtered out in step S2, configuring the frontier point as a target point, and then controlling the robot to move from the current position to the target point, thereby building a local map.