Flocks of birds, shoals of fish, colonies of ants – nature knows how to combine hundreds or thousands of components to large networked systems with amazing capabilities. This serves as inspiration for technical systems that need to work in a self-organized way in dynamic environments. Natural systems are often resilient on losing one or more members, their coordination scales for a wide range of different system sizes, and they are able to adapt their behavior even in case of unpredicted incidents. Lakeside Labs aims at getting the concept of self-organization integrated into mobile robotics and other cyber-physical systems.
Though we can list numerous examples of self-organization in nature and society, there is no straightforward method for designing and testing how microscopic rules influence the macroscopic behavior of an artificial system. In order to solve this problem, the team experiments with biologically-inspired methods, such as swarm intelligence, evolutionary computation, and coupled oscillators.
In the European project CPSwarm, our researchers work on the design of a workbench: a tool chain to model, optimize, simulate, and deploy a swarm of cyber-physical systems. The main contribution for this tool chain is the freely available software tool FREVO.
Another project applies concepts from agent-based swarm modelling to optimize an industrial plant in the semiconductor industry. The main innovation is to reduce the need for pre-calculated schedules or routing tables by applying nature-inspired rules running locally on the individual agents. “This leads to reactive algorithms that are able to compensate for dynamic changes in their local vicinity,” states Melanie Schranz, project lead for this activity.
Distributed simulation for evolutionary design of swarms of cyber-physical systems
Intern. Conf. on Adaptive and Self-Adaptive Systems and Applications, 2018
Evolution as a tool to design self-organizing systems
Lecture Notes in Computer Science on Self-Organizing Systems, 2014
Self-organization in communication networks: Principles and design paradigms
IEEE Communications Magazine, 2005