The main focus of the SYMBRION project is to investigate and develop novel principles of adaptation and evolution for symbiotic multi-robot organisms based on bio-inspired approaches and modern computing paradigms. Such robot organisms consist of super-large-scale swarms of robots, which can dock with each other and symbiotically share energy and computational resources within a single artificial-life-form. When it is advantageous to do so, these swarm robots can dynamically aggregate into one or many symbiotic organisms and collectively interact with the physical world via a variety of sensors and actuators. The bio-inspired evolutionary paradigms combined with robot embodiment and swarm-emergent phenomena, enable the organisms to autonomously manage their own hardware and software organization. In this way, artificial robotic organisms become self-configuring, self-healing, self-optimizing and self-protecting from both hardware and software perspectives. This leads not only to extremely adaptive, evolve-able and scalable robotic systems, but also enables robot organisms to reprogram themselves without human supervision and for new, previously unforeseen, functionality to emerge. In addition, different symbiotic organisms may co-evolve and cooperate with each other and with their environment.

• platform for exploring artificial evolution and pervasive evolve-ability
• extremely powerful computational on-board resources
• support for artificial immunology and embryology
• large number of light modules

Recently, scientific focus was embodied, mobile, autonomous agent controlled by our artificial homeostatic hormone system (AHHS). It is essential to control actuators appropriately for a faced task like locomotion. If several such agents have to cooperate, the coordination of actions becomes important. We investigated the control of both, a single robot as well a set of cooperating modules in multi-modular reconfigurable robotics. Evolutionary computation is used to adapt the controllers in different environmental conditions and tasks. Two such tasks in different environments can be seen in the figures below.

In the following figure locomotion of an organism consisting of three modules can be seen. The environment consists of an empty arena and is implemented in the Symbricator3D simulation environment (physics included). Each of the three module is controlled by an AHHS.