Autonomous Agents and Multi-Agent Systems

, Volume 31, Issue 2, pp 332–361 | Cite as

Interacting with team oriented plans in multi-robot systems

  • Alessandro Farinelli
  • Masoume M. Raeissi
  • Nicolo’ Marchi
  • Nathan Brooks
  • Paul Scerri


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.


Human Operator Output Event Plan Execution Input Event Transition Fire 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of VeronaVeronaItaly
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.Platypus LLCPittsburghUSA

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