Optimal Multi-Robot Task Planning: From Synthesis to Execution (and Back)
Abstract
Integrated task planning and execution is a challenging problem with several applications in AI and robotics. In this work we consider the problem of generating and executing optimal plans for multi-robot systems under temporal and ordering constraints. More specifically, we propose an approach that unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal solutions for task planning, and runtime feedback on their execution.
Cite
Text
Leofante. "Optimal Multi-Robot Task Planning: From Synthesis to Execution (and Back)." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/829Markdown
[Leofante. "Optimal Multi-Robot Task Planning: From Synthesis to Execution (and Back)." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/leofante2018ijcai-optimal/) doi:10.24963/IJCAI.2018/829BibTeX
@inproceedings{leofante2018ijcai-optimal,
title = {{Optimal Multi-Robot Task Planning: From Synthesis to Execution (and Back)}},
author = {Leofante, Francesco},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5771-5772},
doi = {10.24963/IJCAI.2018/829},
url = {https://mlanthology.org/ijcai/2018/leofante2018ijcai-optimal/}
}