Robot exploration mapping method, related equipment and computer program product

By dynamically updating the scope of random numbers and optimizing the sampling strategy using quadrant statistics, the RRT algorithm is improved, solving the problem of inefficient exploration caused by fixed scope and achieving high efficiency and global coverage for robot exploration and mapping.

CN121783115APending Publication Date: 2026-04-03IFLYTEK (SUZHOU) TECH CO LTD
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Patent Information

Application Number
CN202511916893.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-18
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

In the process of robot exploration and mapping, the existing RRT algorithm has low exploration efficiency due to the random sampling strategy with a fixed scope, especially in the early stage of exploration when there are many invalid sampling points. Furthermore, the difference between the scope set by human experience and the actual exploration area further reduces the efficiency.

Method used

The system employs a dynamic update of the random number scope, adjusting the size of the random number scope based on the expansion of the explored map. Furthermore, it improves the effectiveness of sampling points and exploration efficiency through quadrant division and sampling probability optimization.

Benefits of technology

In the early stages of exploration, effective sampling points closer to the robot's current location are generated, reducing the number of iterations, improving map exploration efficiency, and ensuring global coverage.

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Abstract

The invention discloses a robot exploration mapping method, related equipment and a computer program product, a traditional RRT algorithm is improved in the robot exploration mapping process, the size of a random number action range is dynamically updated according to a map explored by a robot at the current moment, and the robot exploration mapping efficiency is improved. In other words, the action range of the random number is expanded along with expansion of the explored map, so that sampling points can be randomly generated in the relatively small action range of the random number at the initial stage of exploration, and the expansion direction of the rapid exploration random tree is guided to generate candidate exploration points, so that the randomly generated sampling points are closer to the current position of the robot; that is, the sampling point is more meaningful to the RRT algorithm, and effective exploration points can be generated through a small number of iterations, so that the map exploration efficiency is improved. And moreover, the action range of the random number is expanded along with the expansion of the explored map, so that the global coverage of exploration can also be ensured.
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