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Dynamic task allocation for multi-robot search and retrieval tasks

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Abstract

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. For example, in a search and rescue domain, a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing such a search and retrieval task by more than one robot increases performance if they are able to distribute their workload efficiently and evenly. In this work, we study the Multi-Robot Task Allocation (MRTA) problem in the search and retrieval domain, where a team of robots is required to cooperatively search for targets of interest in an environment and also retrieve them back to a home base. In comparison with typical foraging tasks, we look at a more general search and retrieval task in which the targets are distinguished with various types, and task allocation also requires taking into account temporal constraints on the team goal. As usual, robots have no prior knowledge about the location of targets in the environment but in addition they need to deliver targets to the home base in a specific order according to their types, which significantly increases the complexity of a foraging problem. We first use a graph-based model to analyse the search and retrieval problem and the dynamics of exploration and retrieval within a cooperative team. We then proceed to present an extended auction-based approach, as well as a prediction approach. The essential difference between these two approaches is that the task allocation and execution procedures in the auction approach are running in parallel, whereas a robot in the prediction approach only needs to choose a task to perform when it has no thing to do. The auction approach uses a winner determination mechanism to allocate tasks to each robot, whereas the robots in the prediction approach implicitly coordinate their activities by team reasoning that leads to consensuses about task allocation. We use the Blocks World for Teams (BW4T) simulator to evaluate the two approaches in our experimental study.

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Notes

  1. As described in [16, 17], each robot can consider all positive and negative synergies, in which two targets have positive (negative) synergy for a robot if the minimum travel cost for visiting them is smaller (larger) than the simple sum of distances from the robot’s current location to each target.

  2. Note that only a subset of vertices in \(\mathcal {G}\) indicates the possible locations of objects, which can be motivated by urban search and rescue applications. For instance, even though robots can have a map of a village, they may not know the precise locations of the survivors after an earthquake.

  3. The BW4T introduced in [14] has been integrated into the agent environments in Goal [12], and more information of it can be found from https://github.com/eishub/BW4T.

  4. Goal version 8024 can be downloaded from http://ii.tudelft.nl/trac/goal/wiki/Releases.

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Acknowledgments

This research was partially supported by the Fundamental Research Funds for the Central Universities (2015B30114). We would like to thank the anonymous reviewers for providing us with constructive comments and suggestions.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Hohai University, NO.200 Jinling Bei Road, Changzhou, 213022, People’s Republic of China

    Changyun Wei

  2. Interactive Intelligence Group, Delft University of Technology, Mekelweg 4, Delft, The Netherlands

    Koen V. Hindriks & Catholijn M. Jonker

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  1. Changyun Wei
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Correspondence to Changyun Wei.

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Wei, C., Hindriks, K.V. & Jonker, C.M. Dynamic task allocation for multi-robot search and retrieval tasks. Appl Intell 45, 383–401 (2016). https://doi.org/10.1007/s10489-016-0771-5

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