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Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms

Autonomous Robotics Research Centre (ARRC), Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Institute of Cognitive Sciences and Technologies (ISTC), Italian National Research Council (CNR), 00185 Rome, Italy
Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
Bitcraze AB, 21222 Malmö, Sweden
Author to whom correspondence should be addressed.
Academic Editor: Álvaro Gutiérrez
Appl. Sci. 2021, 11(7), 3115;
Received: 28 February 2021 / Revised: 22 March 2021 / Accepted: 26 March 2021 / Published: 31 March 2021
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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. View Full-Text
Keywords: swarm robotics; decision-making; task allocation; path finding swarm robotics; decision-making; task allocation; path finding
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MDPI and ACS Style

Albani, D.; Hönig, W.; Nardi, D.; Ayanian, N.; Trianni, V. Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms. Appl. Sci. 2021, 11, 3115.

AMA Style

Albani D, Hönig W, Nardi D, Ayanian N, Trianni V. Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms. Applied Sciences. 2021; 11(7):3115.

Chicago/Turabian Style

Albani, Dario, Wolfgang Hönig, Daniele Nardi, Nora Ayanian, and Vito Trianni. 2021. "Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms" Applied Sciences 11, no. 7: 3115.

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