Robot Exploration Path Planning Using Point Cloud Confidence Maps
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Solution Overview
Problem
Traditional robot exploration methods, such as potential field and frontier point methods, are ineffective in unknown outdoor environments with concentrated obstacles and open spaces, leading to incomplete scanning and loss of direction.
Innovation Solution
A method that establishes a point cloud scene from lidar data, classifies data points into ground and obstacle points, generates a confidence map to identify high-confidence areas, and creates an exploration path using access nodes, allowing the robot to adaptively explore and scan under-scanned areas in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional potential field method is used to push robot forward through resultant force between obstacles, then robot can navigate through narrow channels in indoor environments, but robot will stay in open areas and cannot fully scan in outdoor environments with concentrated obstacles
Solution Approach 1:
The patent transforms the static exploration target into dynamic frontier points that continuously update based on robot position and scanning coverage. The frontier points are dynamically generated at the boundaries between scanned and unscanned areas, allowing the robot to adaptively adjust its navigation targets as it moves through environments with varying obstacle distributions, thus resolving the contradiction between channel navigation and open area scanning capability
Solution Approach 2:
The patent introduces frontier points as intermediary targets between the robot and final exploration goals. These frontier points serve as intermediate navigation markers that guide the robot from current position to unscanned areas, enabling the robot to systematically progress through both narrow channels and open outdoor spaces by continuously generating new frontier points at scan boundaries
2Adaptability or versatility
If frontier point method sets target at outermost periphery of exploration area, then robot can maintain exploration direction, but method loses direction in completely open environments
Solution Approach 1:
The patent applies local quality by generating frontier points specifically at the boundaries between scanned and unscanned areas rather than uniformly across the environment. This localized approach ensures that exploration targets are always positioned at meaningful transition zones, providing continuous directional guidance in both structured environments with channels and completely open areas, thus resolving the directional loss problem in open environments
3Adaptability or versatility
If random walk method is used to fill and cover scene, then robot can eventually cover entire scene, but method requires clear destination location and is inefficient
Solution Approach 1:
The patent implements feedback by continuously monitoring the robot's scanning coverage and using this information to generate new frontier points. The system feedback loop processes the scanned area information, identifies unscanned regions, and generates appropriate navigation targets, enabling the robot to efficiently progress through the environment without random wandering, thus dramatically improving exploration productivity while maintaining complete scene coverage capability
Data Source
AI summary
A method for generating a robot exploration path for a robot to move along includes: establishing a point cloud scene according to single-frame point cloud data collected by the robot, the point cloud scene includes a plurality of data points; classifying and marking each of the data points, the classification mark includes a ground point and an obstacle point, and taking the data points marked as ground points as a ground point cloud; generating a confidence map according to the ground point cloud, the confidence map is configured to display a location and a confidence of each of the ground points in the ground point cloud; generating a plurality of access nodes according to the confidence map; and generating an exploration path according to the access nodes.