Online Reconfiguration of Distributed Robot Control Systems for Modular Robot Behavior Implementation

Malte Wirkus; Sascha Arnold; Elmar Berghöfer

In: Journal of Intelligent & Robotic Systems (JIRS), Vol. 100, No. 3, Pages 1283-1308, Springer Publishing, 12/2020.


The use of autonomous robots in areas that require executing a broad range of different tasks is currently hampered by the high complexity of the software that adapts the robot controller to different situations the robot would face. Current robot software frameworks facilitate implementing controllers for individual tasks with some variability, however, their possibilities for adapting the controllers at runtime are very limited and don't scale with the requirements of a highly versatile autonomous robot. With the software presented in this paper, the behavior of robots is implemented modularly by composing individual controllers, between which it is possible to switch freely at runtime, since the required transitions are calculated automatically. Thereby the software developer is relieved of the task to manually implement and maintain the transitions between different operational modes of the robot, what largely reduces software complexity for larger amounts of different robot behaviors. The software is realized by a model-based development approach. We will resent the metamodels enabling the modeling of the controllers as well as the runtime architecture for the management of the controllers on distributed computation hardware. Furthermore, this paper introduces an algorithm that calculates the transitions between two controllers. A series of technical experiments verifies the choice of the underlying middleware and the performance of online controller reconfiguration. A further experiment demonstrates the applicability of the approach to real robotics applications.


Weitere Links

Wirkus2020_Article_OnlineReconfigurationOfDistrib.pdf (pdf, 3 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz