Thomas Schmickl

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.

Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm

Abstract This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm’s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.

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.

Costs of Environmental Fluctuations and Benefits of Dynamic Decentralized Foraging Decisions in Honey Bees

Honey bees show the impressive ability to choose collectively (through swarm intelligence) between nectar sources of different quality by selecting the energetically optimal one. We here present results from a multi-agent simulation of a cohort of foraging bees. The model, which is built on proximate individual mechanisms, leads to interesting results on the (global) colony level. The simulation allows us to investigate collective foraging decisions in a variety of experimental setups that can be reproduced experimentally with real bees. Because our model allows us to project the daily net honey gain of the simulated honey bee colony, it enables us to explore the economic results of foraging decisions, even in a fluctuating environment. We used the model to investigate the dynamics and efficiency of a bee colony’s decentralized decision system in terms of minimizing the potential cost of lost nectar income due to changes in food quality in a fluctuating environment.

Standard methods for behavioural studies of Apis mellifera

Summary In this BEEBOOK paper we present a set of established methods for quantifying honey bee behaviour. We start with general methods for preparing bees for behavioural assays. Then we introduce assays for quantifying sensory responsiveness to gustatory, visual and olfactory stimuli. Presentation of more complex behaviours like appetitive and aversive learning under controlled laboratory conditions and learning paradigms under free-flying conditions will allow the reader to investigate a large range of cognitive skills in honey bees. Honey bees are very sensitive to changing temperatures. We therefore present experiments which aim at analysing honey bee locomotion in temperature gradients. The complex flight behaviour of honey bees can be investigated under controlled conditions in the laboratory or with sophisticated technologies like harmonic radar or RFID in the field. These methods will be explained in detail in different sections. Honey bees are model organisms in behavioural biology for their complex yet plastic division of labour. To observe the daily behaviour of individual bees in a colony, classical observation hives are very useful. The setting up and use of typical observation hives will be the focus of another section. The honey bee dance language has important characteristics of a real language and has been the focus of numerous studies. We here discuss the background of the honey bee dance language and describe how it can be studied. Finally, the mating of a honey bee queen with drones is essential to survival of the entire colony. We here give detailed and structured information how the mating behaviour of drones and queens can be observed and experimentally manipulated. The ultimate goal of this chapter is to provide the reader with a comprehensive set of experimental protocols for detailed studies on all aspects of honey bee behaviour including investigation of pesticide and insecticide effects.

Micromanipulation, communication and swarm intelligence issues in a swarm microrobotic platform

Abstract Rapid advancements of both microsystem technology and multi-agent systems have generated a new discipline, arising from the fusion of microrobotics technologies and of swarm intelligence theories. Microrobotics contributes with new capabilities in manipulating objects in the microscale and in developing miniaturized intelligent machines, while swarm intelligence supplies new algorithms allowing sets of simple robotic agents to solve complex tasks. A microrobotic swarm that is able to collectively achieve a cleaning task in an arena has been developed. This paper presents a novel platform for microrobotic swarms with the goal to apply swarm intelligence results to practical micromanipulation tasks and describes in details two main features of the platform: an optical communication strategy between the microrobotic agents, in order to share information and to coordinate swarm actions, and a micromanipulation technique–based on electrostatic phenomena–which can be performed by each microrobotic agent.

Antbots: a feasible visual emulation of pheromone trails for swarm robots

In this paper we present an experimental setup to model the pheromone trail based foraging behaviour of ants using a special phosphorescent glowing paint. We have built two custom addons for the e-puck robot that allow for trail laying and following on the glowing floor, as well as a way for the robots to mimic the ants capability of using polarization patterns as a means of navigation. Using simulations we show that our approach allows for efficient pathfinding between nest and potential food sources. Experimental results show that our trail and sun compass add-on boards are accurate enough to allow a single robot to lay and follow a trail repeatedly.

A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning

For any embodied, mobile, autonomous agent it is essential to control its actuators appropriately for the faced task. This holds for natural organisms as well as for robots. If several such agents have to cooperate, the coordination of actions becomes important. We present an artificial homeostatic hormone system which is a bio-inspired control paradigm. It allows to control both, a single robot as well a set of cooperating modules in multi-modular reconfigurable robotics. Our approach is inspired by chemical signal-processing and hormone control in animals. Evolutionary computation is used to adapt controllers for two distinct morphological robot configurations (uni-and multi-modular), different environmental conditions, and tasks. This approach is compared to artificial neural networks. Our results indicate, that the proposed control paradigm is well adaptable to different robot morphologies and to different environmental situations. It is able to generate behaviors for several robotic tasks and outperforms neural networks in terms of evolvability in the tested multi-modular robotic setting tested.