3D multi-robot patrolling with a two-level coordination strategy


Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.

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  1. 1.


  2. 2.

    This can be a rotating laser range-finder or a full 3D scanner.

  3. 3.

    That is, the distance between the closest pair of points of the two planned paths is smaller than \(D_s\).

  4. 4.

    We use an “is_” prefix to denote boolean variables.

  5. 5.

    Or at a pre-fixed frequency, after a first selected is broadcast along the way to the current goal.

  6. 6.

    In our case, this depends on the idlenesses of the nodes.

  7. 7.

    The metric level modules must run on the robot main board and share computational resources with other demanding processing nodes (Kruijff-Korbayová et al. 2015).

  8. 8.

    At this stage, we found this approach to perform very well in practice without significantly limiting the robot manoeuvres in the tested scenarios.

  9. 9.

    Here we include the segmented obstacles in the map and the most recent nearby obstacle points which have been detected by the rangefinder and are not segmented yet in the map.

  10. 10.

    The sub-optimality of the solution is due to the used incremental sampling-based approach (Karaman and Frazzoli 2010; Diankov and Kuffner 2007).

  11. 11.

    As explained in Sect. 7.2, each point of \(\mathcal{M}_t\) can be associated to a robot pose.

  12. 12.

    This can be used for instance to steer the robot toward regions where an estimated WIFI radio signal strength map returns higher values (Caccamo et al. 2017).

  13. 13.

    The dynamic update of the OctoMap and its reactive behaviour is demonstrated in a video https://youtu.be/caECYcYdrgo.

  14. 14.


  15. 15.

    This aspect can be managed for instance as proposed in Zimmermann et al. (2014) and Colas et al. (2013).

  16. 16.


  17. 17.

    Two simulation videos are available on our website and show these behaviour.

  18. 18.

    Since V-REP simulations are computationally demanding in our setup, the simulated robots were not able to move in real time and their motions were very slow (this can be observed in our simulation videos on our website). As a result, when robots got in interference, they persisted in such conditions for longer times with respect to a normal real time simulation.

  19. 19.

    Which we do not report here in order to reduce space.

  20. 20.

    This is not visible in the plot but it was observed by inspecting the recorded data.

  21. 21.


  22. 22.

    In these cases, the path planner only considers the most interesting and useful part of the traversability map.

  23. 23.

    This laser proximity checker inhibits forward velocity commands when a close front obstacle is detected by the laser.

  24. 24.

    In our setup, V-REP is not able to stably simulate more than four robots under realistic conditions (cfr. Sect. 10.1).

  25. 25.

    The path and idleness message sizes actually depends on the number of patrolling graph nodes.

  26. 26.

    Recurring to simpler and more affordable robotic platforms is required.

  27. 27.



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This work was supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under the TRADR Project No. FP7-ICT-609763.

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Correspondence to Luigi Freda.

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Code implementation

For the implementation of the patrolling agent algorithm, we used the C\({++}\) ROS package patrolling_sim as a starting point (Portugal 2017; Portugal and Rocha 2016). This is specifically designed for 2D patrolling tasks. It was used as a starting skeleton architecture providing core functionalities (such as graph management utilities). We significantly modified the core of this package in order to manage 3D data, implement our new patrolling agent algorithm, interface the agent module more tightly with the path planner and the 3D GUI in our network architecture.

An open source implementation of our framework is available.Footnote 27

Software design

A functional diagram of the presented multi-robot system is reported in Fig. 14. The main blocks are listed below.

The robots, each one with its own ID \(\in \{1,\ldots ,m\}\), have the same internal architecture and host the on-board functionalities which concern decision and processing aspects both at topological level and at metric level. According to Sect. 4.5, each robot maintains and updates an instance of the patrolling graph and of the metric map in its internal memory.

The core services, hosted in the main central computer, manage the multi-robot system persistence database and allow specific modules to load/save map, trajectories and patrolling graphs from/into the central database (for re-using relevant data along different missions).

The core modules, also hosted in central computer, include the patrolling graph builder and the patrolling monitor. The first builds a patrolling graph from a user assigned set of waypoints or from a saved history of robot trajectories. The built patrolling graph is then distributed to all the robots and saved in the central persistence database. The patrolling monitor continuously checks the current status of the patrolling activities and records relevant data for monitoring and benchmarking.

The multi-robot 3D GUI, hosted on one OCU (Operator Control Unit), is based on RVIZ and allows the user (i) to select multiple waypoints which can be fed to the path planners or to the patrolling graph builder (ii) to visualize relevant point cloud data, maps, and robot models (iii) to stop/restart robots when needed (iv) to trigger the loading/saving of maps and robot trajectories (v) to realign the current map of a selected robot to a loaded map.

The architecture is fully distributed without centralized coordination mechanisms. In particular, each robot hosts an instance of the patrolling agent and of the path-planner.

As shown in Fig. 14, the various modules in the architecture exchange different kind of messages. These are grouped in the following types.

  • Coordination messages These are exchanged amongst robots for sharing knowledge and decisions, in order to attain cooperation and coordination. For convenience, the patrol monitor records an history of these messages.

  • GUI messages These are exchanged with the 3D GUI and include both control messages and visualization data.

  • Load/save messages: these are exchanged with the core services and contain both loaded and saved data.

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Freda, L., Gianni, M., Pirri, F. et al. 3D multi-robot patrolling with a two-level coordination strategy. Auton Robot 43, 1747–1779 (2019). https://doi.org/10.1007/s10514-018-09822-3

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  • 3D patrolling
  • 3D multi-robot systems
  • Distributed multi-robot coordination
  • UGVs