Albani, Dario; Hönig, Wolfgang; Nardi, Daniele; Ayanian, Nora; Trianni, Vito (2021): Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms. In: Applied Sciences, 11 (7), pp. 3115, 2021.(Type: Journal Article | Abstract | BibTeX | Tags: | Links: )
@article{Albani_2021,
title = {Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms},
author = {Dario Albani and Wolfgang Hönig and Daniele Nardi and Nora Ayanian and Vito Trianni},
url = {https://www.mdpi.com/2076-3417/11/7/3115},
doi = {10.3390/app11073115},
year = {2021},
date = {2021-03-31},
journal = {Applied Sciences},
volume = {11},
number = {7},
pages = {3115},
publisher = {MDPI AG},
abstract = {Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.},
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}
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
Albani, Dario; Manoni, Tiziano; Arik, Arikhan; Nardi, Daniele; Trianni, Vito (2019): Field coverage for weed mapping: toward experiments with a UAV swarm. In: Proceedings of the 11th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2019), pp. 1–16, EAI Pittsburg, US, 2019.(Type: Inproceeding | BibTeX | Tags: | Links: )
@inproceedings{AlbaniEtAl:BICT2019,
title = {Field coverage for weed mapping: toward experiments with a UAV swarm},
author = { Dario Albani and Tiziano Manoni and Arikhan Arik and Daniele Nardi and Vito Trianni},
url = {http://laral.istc.cnr.it/trianni/wp-content/uploads/2019/05/BICT2019.pdf},
year = {2019},
date = {2019-03-01},
booktitle = {Proceedings of the 11th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2019)},
pages = {1--16},
address = {Pittsburg, US},
organization = {EAI},
keywords = {},
pubstate = {published},
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Albani, Dario; H"onig, Wolfgang; Ayanian, Nora; Nardi, Daniele; Trianni, Vito (2019): Summary: Distributed Task Assignment and Path Planning with Limited Communication for Robot Teams. In: Proceedings of the th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1770–1772, International Foundation for Autonomous Agents and Multiagent Systems, Montreal QC, Canada, 2019.(Type: Inproceeding | BibTeX | Tags: )
@inproceedings{Albani:2019wl,
title = {Summary: Distributed Task Assignment and Path Planning with Limited Communication for Robot Teams},
author = { Dario Albani and Wolfgang H"onig and Nora Ayanian and Daniele Nardi and Vito Trianni},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {1770--1772},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Montreal QC, Canada},
keywords = {},
pubstate = {published},
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Albani, Dario; Nardi, Daniele; Trianni, Vito (2017): Field coverage and weed mapping by UAV swarms. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 4319–4325, IEEE, 2017.(Type: Inproceeding | BibTeX | Tags: | Links: )
@inproceedings{Albani:2017cb,
title = {Field coverage and weed mapping by UAV swarms},
author = { Dario Albani and Daniele Nardi and Vito Trianni},
url = {http://laral.istc.cnr.it/trianni/wp-content/uploads/2018/02/albani-iros2017.pdf},
doi = {10.1109/IROS.2017.8206296},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS},
pages = {4319--4325},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Albani, Dario; IJsselmuiden, Joris; Haken, Ramon; Trianni, Vito (2017): Monitoring and mapping with robot swarms for agricultural applications. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, IEEE, 2017.(Type: Inproceeding | BibTeX | Tags: | Links: )
@inproceedings{Albani:2017tp,
title = {Monitoring and mapping with robot swarms for agricultural applications},
author = { Dario Albani and Joris IJsselmuiden and Ramon Haken and Vito Trianni},
url = {http://laral.istc.cnr.it/trianni/wp-content/uploads/2018/02/ITEMS2017-ID11.pdf},
doi = {10.1109/AVSS.2017.8078478},
year = {2017},
date = {2017-01-01},
booktitle = {2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
pages = {1--6},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
)