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Interacting with team oriented plans in multi-robot systems

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Abstract

Team oriented plans have become a popular tool for operators to control teams of autonomous robots to pursue complex objectives in complex environments. Such plans allow an operator to specify high level directives and allow the team to autonomously determine how to implement such directives. However, the operators will often want to interrupt the activities of individual team members to deal with particular situations, such as a danger to a robot that the robot team cannot perceive. Previously, after such interrupts, the operator would usually need to restart the team plan to ensure its success. In this paper, we present an approach to encoding how interrupts can be smoothly handled within a team plan. Building on a team plan formalism that uses Colored Petri Nets, we describe a mechanism that allows a range of interrupts to be handled smoothly, allowing the team to efficiently continue with its task after the operator intervention. We validate the approach with an application of robotic watercraft and show improved overall efficiency. In particular, we consider a situation where several platforms should travel through a set of pre-specified locations, and we identify three specific cases that require the operator to interrupt the plan execution: (i) a boat must be pulled out; (ii) all boats should stop the plan and move to a pre-specified assembly position; (iii) a set of boats must synchronize to traverse a dangerous area one after the other. Our experiments show that the use of our interrupt mechanism decreases the time to complete the plan (up to 48 % reduction) and decreases the operator load (up to 80 % reduction in number of user actions). Moreover, we performed experiments with real robotic platforms to validate the applicability of our mechanism in the actual deployment of robotic watercraft.

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Notes

  1. See for example CPN Tools [18].

  2. With the term proxy we refer to a software-service that connects a specific boat with the rest of the system.

  3. Recall from Sect. 3.1 that output events are associated to places and contain commands or requests for other modules. Input events are associated to transitions and encapsulate information that should be consumed by the module that receives such event.

  4. Since computing the minimum path cost given a sequence of visit locations is in general NP-Hard here we use a simple nearest neighbor heuristic: the path is built incrementally by always selecting the next location as the one that is closest to the current location. At the beginning the current location is the boat position.

  5. While in our case the number of proxy / generic tokens is always finite, we might not know this number before the plan starts. Hence we use the inhibitor arc to check whether a place is empty.

  6. To check whether results are statistically significant we run a t-test with \(\alpha = 0.05\).

  7. According to a t-test with \(\alpha = 0.05\), the total time gain for the reassignment versions of the interrupt versus standard plan is not statistically significant, so we do not report such metric in the table.

  8. http://profs.sci.univr.it/~farinelli/videos/CLV.mp4.

  9. https://youtu.be/l5Qhp1JSoNI

  10. This video was accepted to the IJCAI 2015 video competition.

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Acknowledgments

This work is partially funded by the Qatar National Research Fund NPRP grant 4-1330-1-213. This work is partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689341. This work reflects only the authors’ view and the EASME is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Computer Science Department, University of Verona, Verona, CAP 37134, Italy

    Alessandro Farinelli, Masoume M. Raeissi & Nicolo’ Marchi

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Nathan Brooks

  3. Platypus LLC, Pittsburgh, PA, USA

    Paul Scerri

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  1. Alessandro Farinelli
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  2. Masoume M. Raeissi
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  3. Nicolo’ Marchi
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  5. Paul Scerri
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Correspondence to Alessandro Farinelli.

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Farinelli, A., Raeissi, M.M., Marchi, N. et al. Interacting with team oriented plans in multi-robot systems. Auton Agent Multi-Agent Syst 31, 332–361 (2017). https://doi.org/10.1007/s10458-016-9344-6

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