A multi-robot path planning method and system based on hierarchical control

By employing a hierarchical control-based multi-robot path planning method, combined with SLAM and TEB+VO algorithms, the problem of path non-executability caused by neglecting robot posture in existing technologies is solved. This achieves efficient and reliable multi-robot path planning, improving obstacle avoidance accuracy and system stability.

CN120560258BActive Publication Date: 2026-08-04XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202510683858.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-05-26
Publication Date
2026-08-04
Estimated Expiration
2045-05-26

AI Technical Summary

Technical Problem

Existing multi-robot path planning methods neglect the actual robot posture in practical engineering applications, resulting in unexecutable paths, significant cumulative error impact, sharp speed changes, and robot performance degradation.

Method used

A multi-robot path planning method based on hierarchical control is adopted. The SLAM algorithm is used to construct a real map, the SH-EECBS algorithm is used for global path planning, and the TEB algorithm and VO method are used for local path planning. The extended Kalman filter and multiple sensors are used to acquire the robot state in real time to generate a continuous and smooth path.

Benefits of technology

It significantly improves the efficiency of multi-robot collaboration, reduces computational complexity and hardware consumption, enhances obstacle avoidance accuracy and path planning reliability, reduces path conflicts and collision rates, and extends the service life of robot systems.

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Abstract

This invention discloses a multi-robot path planning method and system based on hierarchical control. The method acquires model information and basic parameters of each robot in the system, and constructs a realistic map of the working environment using the SLAM algorithm. The grids of the constructed realistic map are merged, transforming a fine-grained grid map into a coarse-grained one. Considering the overall movement direction and target position of the robots, a global path input for the navigation system is obtained. This global path input is published to each robot, and robots can subscribe to other robots' paths to capture their global path information. Each robot perceives local environmental information in a local map and obtains information about obstacles around other robots. Based on the global path results from the MAPF algorithm, a local path planning method is used to generate local paths, enabling avoidance between robots. This provides a reliable solution for efficient collaboration in multi-robot systems.
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