Name
Abstract | ||
|---|---|---|
Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments. Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts. In some cases, these solutions are only able to satisfy the objective of minimizing task time. We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation. In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously. We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning. VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection. Meanwhile, VGTA includes schedule conflict and path conflict handling strategies. In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflzicts between robots when exchanging positions in the adjacent grid cells. Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments. Experimental results indicate that VGTA's search capability and computation cost are satisfactory. Meanwhile, its solutions are able to be really executed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2942966 | IEEE ACCESS |
Keywords | DocType | Volume |
Multi-robot task allocation,cooperative task,schedule conflict,path conflict,unmanned multi-robot swarm | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongguang Zhang | 1 | 106 | 16.70 |
| Han Luo | 2 | 0 | 0.34 |
| Zan Wang | 3 | 0 | 0.34 |
| Yuhong Liu | 4 | 0 | 0.34 |
| Y. Liu | 5 | 578 | 102.76 |