Abstract
Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration ((Formula presented.)), which combines Coordinated Multi-Robot Exploration ((Formula presented.)) and African Vultures Optimization Algorithm ((Formula presented.)) to optimize the construction of a finite map in multi-robot exploration. We compared (Formula presented.) with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.
| Original language | English |
|---|---|
| Article number | 2474 |
| Journal | Mathematics |
| Volume | 11 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Jun 2023 |
Keywords
- African Vulture Optimization Algorithm (AVOA)
- Coordinated Multi-Robot Exploration (CME)
- Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME)
- multi-robot exploration; finite map
- optimization
- path planning
- unknown environments
