Mobile robot path planning method based on artificial mouse travel algorithm
By introducing dynamic adaptive mechanism and local enhanced search strategy, the artificial lemming algorithm is optimized, which solves the problem of local optimality in path planning and improves the search performance and robustness of path planning.
CN120593789APending Publication Date: 2025-09-05GUIZHOU INST OF TECH +1
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Patent Information
- Application Number
- CN202510634305.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-16
- Publication Date
- 2025-09-05
AI Technical Summary
Technical Problem
The existing artificial lemming algorithm lacks adaptive mechanisms and local search capabilities in path planning, which may lead to falling into local optimal solutions and insufficient search capabilities.
Method used
A dynamic adaptive mechanism, a hybrid Nelder-Mead method and a small-scale perturbation strategy are introduced to optimize path planning by dynamically adjusting the search range and introducing local enhanced search, combining Brownian motion, spiral search, Lévy flight and Gaussian perturbation.
Benefits of technology
It improves the search performance of path planning, enhances the robustness and path optimization capabilities in complex environments, avoids falling into local optimality, and improves the accuracy and efficiency of path planning.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a robot path planning method based on an artificial mouse travel algorithm. The method comprises the following steps: constructing a map; the method comprises the steps of parameter setting, initialization, initial path fitness evaluation, global search, local search, new path evaluation and local enhancement search, a dynamic adaptive search radius strategy is used for dynamically adjusting a search range C, and when an exploration-development switching threshold value E is larger than 1, namely Egt; the method comprises the following steps: 1, starting global search by adopting a dual-mode hybrid strategy, Elt; the method comprises the following steps: 1, starting local search, adopting an adaptive probability Levy flight or small-range disturbance strategy, introducing a local search enhancement module, carrying out refined search on a current global optimal path Xbest through a simplex method Nelder-Mead, and evaluating the fitness of the optimal path of iteration each time through the algorithm; and outputting an optimal path sequence according to the fitness value of the recorded optimal path. The method has the characteristics of improving the search capability, obtaining the optimal path and the like.
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