{"id":1258,"date":"2026-01-25T19:34:06","date_gmt":"2026-01-25T19:34:06","guid":{"rendered":"https:\/\/www.cs.ubbcluj.ro\/~meco\/hybrid-adaptive-greedy-algorithm-addressing-the-multi-robot-path-planning-problem-2025\/"},"modified":"2026-02-01T12:07:23","modified_gmt":"2026-02-01T12:07:23","slug":"hybrid-adaptive-greedy-algorithm-addressing-the-multi-robot-path-planning-problem-2025","status":"publish","type":"post","link":"https:\/\/www.cs.ubbcluj.ro\/~meco\/hybrid-adaptive-greedy-algorithm-addressing-the-multi-robot-path-planning-problem-2025\/","title":{"rendered":"Hybrid Adaptive Greedy Algorithm Addressing the Multi-Robot Path Planning Problem (2025)"},"content":{"rendered":"
\n

IEEE Latin America Transactions<\/p>\n

Authors<\/h2>\n

Anik\u00f3 Kopacz, Enol Garc\u00eda Gonz\u00e1lez, Camelia Chira, Jos\u00e9 Ram\u00f3n Villar Flecha<\/p>\n

Abstract<\/h2>\n

In the past few years, path planning and scheduling became a high-impact research topic due to their real-world applications such as transportation, manufacturing and robotics. This paper focuses on the Multi-robot Path Planning (MPP) problem, which consists of planning the route for a set of robots in a given static environment. The main goal is to navigate the robots from a starting point to a destination point without colliding with other robots or static obstacles. We propose a hybrid method — H* — that combines adaptive route planning based on A* and local search algorithm to optimize routes in the context of the MPP problem. The A* algorithm finds the optimal solution for the route search problem and a heuristic approach is applied to scale up to the multi-agent scenario.The overall length of determined paths and the number of robot collisions is minimized during the evaluations specific small-scale environments.Computational experiments are conducted for multi-robot scenarios and the performance of H* is compared to several path-searching algorithms including A* variations extended for the multi-agent scenario and coevolutionary algorithms.Experimental results demonstrate that H* outperforms the A* based heuristic approaches in terms of path length. H* shows similar performance as the coevolutionary method and performs better on smaller-scale maps.<\/p>\n

Citation<\/h2>\n
@Inproceedings{Kopacz2025HybridAG,\n author = {Anik\u00f3 Kopacz and Enol Garc\u00eda Gonz\u00e1lez and Camelia Chira and Jos\u00e9 Ram\u00f3n Villar Flecha},\n booktitle = {IEEE Latin America Transactions},\n title = {Hybrid Adaptive Greedy Algorithm Addressing the Multi-Robot Path Planning Problem},\n year = {2025}\n}<\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
In the past few years, path planning and scheduling became a high-impact research topic due to their real-world applications such as transportation, manufacturing and robotics. This paper focuses on the Multi-robot Path Planning (MPP) problem, which consists of planning the route for a set of robots in a given static environment. The main goal is to navigate the robots from a starting point to a destination point without colliding with other robots or static obstacles. We propose a hybrid method \u2014 H* \u2014 that combines adaptive route planning based on A* and local search algorithm to optimize routes in the context of the MPP problem. The A* algorithm finds the optimal solution for the route search problem and a heuristic approach is applied to scale up to the multi-agent scenario.The overall length of determined paths and the number of robot collisions is minimized during the evaluations specific small-scale environments.Computational experiments are conducted for multi-robot scenarios and the performance of H* is compared to several path-searching algorithms including A* variations extended for the multi-agent scenario and coevolutionary algorithms.Experimental results demonstrate that H* outperforms the A* based heuristic approaches in terms of path length. H* shows similar performance as the coevolutionary method and performs better on smaller-scale maps.<\/p>\n","protected":false},"author":6,"featured_media":1039,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[51,10,50],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1258"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/comments?post=1258"}],"version-history":[{"count":2,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1258\/revisions"}],"predecessor-version":[{"id":1423,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1258\/revisions\/1423"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media\/1039"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media?parent=1258"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/categories?post=1258"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/tags?post=1258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}