Serge Kernbach

Re-embodiment of Honeybee Aggregation Behavior in an Artificial Micro-Robotic System

In this article we describe the re-embodiment of biological aggregation behavior of honeybees in Jasmine micro-robots. The observed insect behavior, in the context of the insects sensor—actor system, is formalized as behavioral and motion-sensing meta-models. These meta-models are transformed into a sensor—actor system of micro-robots by means of a sensors virtualization technique. This allows us to keep the efficiency and scalability of the bio-inspired approach. We also demonstrate the systematic character of this re-embodiment procedure on collective aggregation in a real robotic swarm.

Get in touch: cooperative decision making based on robot-to-robot collisions

We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.

Symbiotic robot organisms: REPLICATOR and SYMBRION projects

Cooperation and competition among stand - alone swarm agents can increase the collective fitness of the whole system. An interesting form of collective system is demonstrated by some bacteria and fungi, which can build symbiotic organisms. Symbiotic communities can enable new functional capabilities which allow all members to survive better in their environment. In this article we show an overview of two large European projects dealing with new collective robotic systems which utilize principles derived from natural symbiosis. The paper provides also an overview of typical hardware, software and methodological challenges arose along these projects, as well as some prototypes and on-going experiments available on this stage.

CoCoRo -- The Self-Aware Underwater Swarm

The EU-funded CoCoRo project studies heterogeneous swarms of AUVs used for the purposes of under water monitoring and search. The CoCoRo underwater swarm system will combine bio-inspired motion principles with biologically-derived collective cognition mechanisms to provide a novel robotic system that is scalable, reliable and flexible with respect its behavioural potential. We will investigate and develop swarm-level emergent self-awareness, taking biological inspiration from fish, honeybees, the immune system and neurons. Low-level, local information processing will give rise to collective-level memory and cognition. CoCoRo will develop a novel bio-inspired operating system whose default behaviour will be to provide AUV shoaling functionality and the maintenance of swarm coherence. Collective discrimination of environmental properties will be processed on an individual-or on a collective-level given the cognitive capabilities of the AUVs. We will investigate collective self-recognition through experiments inspired by ethology and psychology, allowing for the quantification of collective cognition.

Evolutionary robotics: The next-generation-platform for on-line and on-board artificial evolution

In this paper we present the development of a new self-reconfigurable robotic platform for performing on-line and on-board evolutionary experiments. The designed platform can work as an autonomous swarm robot and can undergo collective morphogenesis to actuate in different morphogenetic structures. The platform includes a dedicated power management, rich sensor mechanisms for on-board fitness measurement as well as very powerful distributed computational system to run learning and evolutionary algorithms. The whole development is performed within several large European projects and is open-hardware and open-software.

Embodied Artificial Evolution - Artificial Evolutionary Systems in the 21st Century

Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from passively understanding to actively using evolutionary processes. Today this is possible in Evolutionary Computing, where human experimenters can design and manipulate all components of evolutionary processes in digital spaces. We argue that in the near future it will be possible to implement artificial evolutionary processes outside such imaginary spaces and make them physically embodied. In other words, we envision the “Evolution of Things”, rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution (EAE). The main objective of this paper is to present a unifying vision in order to aid the development of this high potential research area. To this end, we introduce the notion of EAE, discuss a few examples and applications, and elaborate on the expected benefits as well as the grand challenges this developing field will have to address.

Collective energy homeostasis in a large-scale microrobotic swarm

This paper introduces an approach that allows swarm robots to maintain their individual and collective energetic homeostasis. The on-board recharging electronics and intelligent docking stations enable the robots to perform autonomous recharging from low energy states. The procedure of collective decision-making increases collective efficiency by preventing bottlenecks at docking stations and the energetic death of low-energy robots. These hardware and behavioral mechanisms are implemented in a swarm of real microrobots, and several analogies to self-regulating biological strategies are found.

Stability of On-Line and On-Board Evolving of Adaptive Collective Behavior

This work focuses on evolving purposeful collective behavior in a swarm of Jasmine micro-robots. We investigate the stability of the on-line and on-board evolutionary approaches, where mutation, crossover as well as fitness calculation are performed only by interacting micro-robots without using any centralized resources. In this work it is demonstrated that the environment-adaptive collective behavior can be obtained, where the evolving fitness and behavior are partially stable. To increase stability of the approach, some reduction methodology of the search space is proposed.

Multi-robot searching algorithm using Lévy flight and artificial potential field

An efficient search algorithm is very crucial in robotic area, especially for exploration missions, where the target availability is unknown and the condition of the environment is highly unpredictable. In a very large environment, it is not sufficient to scan an area or volume by a single robot, multiple robots should be involved to perform the collective exploration. In this paper, we propose to combine bio-inspired search algorithm called Lévy flight and artificial potential field method to perform an efficient searching algorithm for multi-robot applications. The main focus of this work is to prove the concept and to measure the efficiency of the algorithm. Several experiments, which compare different search algorithms, are also performed.

Towards bio-hybrid systems made of social animals and robots

For making artificial systems collaborate with group-living animals, the scientific challenge is to build artificial systems that can perceive, communicate to, interact with and adapt to animals. When such capabilities are available then it should be possible to built cooperative relationships between artificial systems and animals. Machines In this framework, machines do not replace the living agents but collaborate and bring new capabilities into the resulting mixed group. On the one hand, such artificial systems offer new types of sensors, actuators and communication opportunities for living systems; on the other hand the animals bring their cognitive and biological capabilities into the artificial systems. Novel bio-hybrid modeling frameworks should be developed to streamline the implementation issues and allow for major time saving in the design and building processes of artificial agents. We expect strong impacts on the design of new intelligent systems by merging the best of the living systems with the best of ICT systems.