Abstract
This work demonstrates how dynamic robot behaviour that responds to different types of network disturbances can improve communication and mission performance in a Multi-Robot Team (MRT). A series of experiments are conducted which show how two different network perturbations (i.e. packet loss and signal loss) and two different network types (i.e. wireless local area network and ad-hoc network) impact communication. Performance is compared using two MRT behaviours: a baseline versus a novel dynamic behaviour that adapts to fluctuations in communication quality. Experiments are carried out on a known map with tasks assigned to a robot team at the start of a mission. During each experiment, a number of performance metrics are recorded. A novel dynamic Leader-Follower (LF) behaviour enables continuous communication through two key functions: the first reacts to the network type by using signal strength to determine if the robot team must commit to grouping together to maintain communication; and the second employs a special task status messaging function that guarantees a message is communicated successfully to the team members. The results presented in this work are significant for real-world multi-robot system applications that require continuous communication amongst team members.
This work was partially supported by a King’s College London Graduate Training Scholarship and by the ESRC under grant #ES/P011160/1.
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Notes
- 1.
A “topic” in a Publisher-Subscriber (Pub-Sub) communication system refers to a category of related messages that are defined and grouped together when the applied system is engineered.
- 2.
Adaptive Monte Carlo localisation (position estimation) for mobile robots [7].
- 3.
- 4.
Currently the ad-hoc network is simulated.
- 5.
- 6.
The “agenda” is the list of tasks a robot has been allocated by the assigner agent.
- 7.
The 2 message topics effected by SPL are TaskStatus and team members’ AmclPose.
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Zhivkov, T., Schneider, E., Sklar, E. (2019). MRComm: Multi-Robot Communication Testbed. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_30
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