<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper considers SUB_GOALs by using basic A* algorithm and Subgoal Graphs in a hybrid approach to execute optimal route. SUB_GOALs identified with pre-searching from basic A* at break points and Subgoal Graphs at corners of obstacles are added to SUB_TABLE to expedite the final searching in the hybrid approach. Map to work on is divided to subregions with decision-making process by using line-of-sight to avoid redundant searching. In the final searching layer, all feasible SUB_GOALs gained from decision-making process in the same subregion are connected to find final solutions of routes. Solutions achieved in the divided subregions are evaluated and combined to discover the final optimal route. The proposed hybrid approach is applied to three different scenarios in various dimensions of maps. In these three scenarios, the shortest route without hitting obstacles is calculated as 46.67, 57.76 and 124.7 units, respectively, and compared with other search algorithms. Simulation results of route planning are demonstrated to exhibit the effectiveness of the proposed hybrid approach.</p>
</abstract>

This paper considers SUB_GOALs by using basic A* algorithm and Subgoal Graphs in a hybrid approach to execute optimal route. SUB_GOALs identified with pre-searching from basic A* at break points and Subgoal Graphs at corners of obstacles are added to SUB_TABLE to expedite the final searching in the hybrid approach. Map to work on is divided to subregions with decision-making process by using line-of-sight to avoid redundant searching. In the final searching layer, all feasible SUB_GOALs gained from decision-making process in the same subregion are connected to find final solutions of routes. Solutions achieved in the divided subregions are evaluated and combined to discover the final optimal route. The proposed hybrid approach is applied to three different scenarios in various dimensions of maps. In these three scenarios, the shortest route without hitting obstacles is calculated as 46.67, 57.76 and 124.7 units, respectively, and compared with other search algorithms. Simulation results of route planning are demonstrated to exhibit the effectiveness of the proposed hybrid approach.

A Hybrid Routing Approach Using Two Searching Layers

Associate Professor, Mechatronics Engineering Department, Technology Faculty

Lecturer, Vocational School of Technical Sciences, Electronics and Automation Department

Electrical, Control and Communication Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Hybrid Routing Approach Using Two Searching Layers"}

This paper considers SUB_GOALs by using basic A* algorithm and Subgoal Graphs in a hybrid approach to execute optimal route. SUB_GOALs identified with...

A Hybrid Routing Approach Using Two Searching Layers

Pubblicato online: 13 set 2021

Pagine: 59 - 66

DOI: https://doi.org/10.2478/ecce-2021-0007

Parole chiave
Motion control, Optimization, Path planning, Shortest path problem

© 2021 Gonca Ozmen Koca et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Hybrid Routing Approach Using Two Searching Layers

Pubblicato online: 13 set 2021

Pagine: 59 - 66

DOI: https://doi.org/10.2478/ecce-2021-0007

Parole chiaveMotion control, Optimization, Path planning, Shortest path problem

© 2021 Gonca Ozmen Koca et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Motion control, Optimization, Path planning, Shortest path problem