Abstract

Robot and multi-robot systems are leaving the friendly well-structured world of automation and are facing the challenges of a dynamic world. Such uncertain conditions call for a high degree of robustness and adaptivity for individual robots as well as for the organization of multi-robot systems. This corresponds to the concepts of self-adaptation and self-organization. Robots adapting to the dynamic environment still have to pursue their given tasks or goals. In order to address the requirements of creating adaptive and goal-driven multi-robot systems, it is necessary to combine existing goal-directed planning and decision-making approaches with self-adaptation and self-organization mechanisms. This work addresses this challenge with a new hybrid approach integrated into a common robot framework, combining symbolic planning with reactive behaviour networks, machine learning, and the pattern-based selection of suitable mechanisms. On that account it brings together the advantages of bottom-up and top-down oriented approaches.

@INPROCEEDINGS{7789484, 
author={Christopher-Eyk Hrabia}, 
booktitle={2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W)}, 
title={A Framework for Adaptive and Goal-Driven Behaviour Control of Multi-robot Systems}, 
year={2016}, 
pages={275-280}, 
keywords={Adaptive systems;Decision making;Multi-robot systems;Planning;Robots;Robustness;Vehicle dynamics;decision-making;multi-robot systems;planning;reinforcement learning;self-adaptation;self-organization}, 
doi={10.1109/FAS-W.2016.67}, 
month={Sept},}