3D Multi-Robot Patrolling with a Two-Level Coordination Strategy
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Authors: Luigi Freda, Mario Gianni, Fiora Pirri, Abel Gawel, Renaud Dubé, Roland Siegwart and Cesar Cadena
Maintainer: Luigi Freda
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.
Documents
"3D Multi-Robot Patrolling with a Two-Level Coordination Strategy"
Luigi Freda · Mario Gianni · Fiora Pirri · Abel Gawel · Renaud Dubé · Roland Siegwart · Cesar Cadena
Autonomous Robots, 2018
Code
The code is available on
https://github.com/luigifreda/3dpatrolling
Simulations
The following simulations are realized within V-REP. The environment is represented by using a point cloud map. The built map is segmented into traversable regions (green) and obstacle regions (red).
N.B.: in the following videos, speed can be increased by using the media player interface.
Path planning with multi-robot traversability
The following two simulations show how our path planner succeeds in managing metric conflicts by using multi-robot traversability. Each robot moves back and forth between its two assigned waypoints. Planned paths (emanated from each robot) are shown in the videos.
path planning and multi-robot traversability in a free area with 3 robots - video
path planning on cyclic paths with three robots
without metric coordination - video (here the basic path planners do not succeed in avoiding a final deadlock without metric coordination)
with metric coordination - video (metric coordination nicely allows the robots to avoid congestion and deadlocks)
Patrolling
The following simulations show our full patrolling strategy with a team of robots. Here, the CC (Coordination Plus Cooperation) strategy is used to guide the robot team. Each node of the patrolling graph is represented by a disc/balloon whose radius grows with time proportionally to the node idleness. When a robot reaches a node, the local "balloon" is deflated (i.e. the idleness is reset). In short, the main mission of the patrolling robots is to plan their motions to continuously visit the nodes and keep the balloons as small as possible.
In our patrolling framework, you can set the priority of each node in the patrolling graph. The priority of a node controls the inflating speed of the local "balloon": the higher the priority, the higher the inflating speed. The following simulations show how the priority can be set by using the system GUI, and the typical behavior of the robots when node priorities are changed.
Experiments
In the following experiments, the environment is represented by using a point cloud map. The built map is segmented in traversable regions (green) and obstacle regions (red). Each node of the patrolling graph is represented by a disc whose radius is proportional to the node idleness. Planned paths are emanated from each robot.
The following patrolling experiments aim at showing the applicability and portability of the developed software in the real world. The CC (Coordination Plus Cooperation) strategy was tested on the TRADR robots.
exp1: 2 robots in DIAG corridor with CC strategy - video (2X speed)
exp2: 2 robots in DIAG corridor: this experiment highlights the dynamic mapping capability of our system - video (2X speed)
exp3: 2 robots in DIAG court with CC strategy - video (2X speed)
Experiments with 3 UGVs:
exp4: 3 robots in DIAG corridor (big graph) with CC strategy - video (2X speed)
exp5: 3 robots in DIAG ramp with CC strategy - video (2X speed)
N.B.
• video speed can be further increased on the media player interface
• in the above videos, the visualized data in RVIZ may sometimes appear delayed: this is due to the transfer and visualization of the point cloud maps on the central user interface; the use of such a GUI is an option and is not necessary to the patrolling system
TRADR EU Project
This work was conducted within the TRADR EU project (No. FP7-ICT-609763). You can find a presentation of the project on its website.
TRADR Review Year 4: on March 21-22 2018, we successfully demoed our patrolling system in a rescue scenario at an industrial site in Mestre, Italy, under the great hospitality of our Italian Firefighters, Vigili del Fuoco.
TRADR Review Year 3: on March 8th 2017, we successfully showed two robots patrolling in a rescue scenario during the third year review of the TRADR EU Project. The review was held in Montelibretti at the "Scuola di Formazione Operativa di Montelibretti" of Vigili del Fuoco (the Italian Firefighters).
Authors
Luigi Freda · Mario Gianni · Fiora Pirri (Alcor Lab - University of Rome "La Sapienza")
Abel Gawel · Renaud Dubé · Roland Siegwart · Cesar Cadena (ASL ETHZ)
Maintainer
This website is maintained by Luigi Freda.
Credits
We would like to thank Indro Spinelli for his great help in performing patrolling experiments with the robots.
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