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apps Other research productkeyboard_double_arrow_right Article 2021 Italy EnglishPublisher:Multidisciplinary Digital Publishing Institute Funded by:EC | TAILOREC| TAILORDario Albani; Wolfgang Hönig; Daniele Nardi; Nora Ayanian; Vito Trianni;doi: 10.3390/app11073115
handle: 11573/1620438
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.
Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
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apps Other research productkeyboard_double_arrow_right Article 2021 Italy EnglishPublisher:Multidisciplinary Digital Publishing Institute Funded by:EC | TAILOREC| TAILORDario Albani; Wolfgang Hönig; Daniele Nardi; Nora Ayanian; Vito Trianni;doi: 10.3390/app11073115
handle: 11573/1620438
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.
Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu