Evolutionary Swarms of Flying Robots

Dario Floreano, Sabine Hauert, Severin Leven, Jean-Christophe ZuffereyLaboratory of Intelligent Systems

Ecole Polytechnique Federale, Lausanne, Switzerland{Name.Surname}@epfl.ch

In case of a wide-area catastrophic event, rescuers often need a dedicatedcommunication system allowingthem to exchange information witha base station and with otherrescuers. In this project, we aredeveloping a swarm of small andlight-weight flying robots that willautonomously locate the rescuersand establish a communicationnetwork with the base station(figure 1). The swarm of robotsshould be able to operate invarious l ighting condit ions,including at night, and maintain thecommunication link if rescuersmove around the area or if somerobot fails.The robot consists of a flying wing equipped with a pressure altimeter, a speedsensor, a magnetic compass, an Inertial Measurement Unit, a wireless radio-link,and a LinuX onboard computer for handling control and communication (thedetailed description and demonstration of the robot can be found in anotherposter presentation [1]). For this project, the robot does not use vision and GPS.However, we are designing the robotic platform in such a way that it can beeasily customized with several sensors and easily programmed by otherresearchers in the biological and engineering communities.In a preliminary scenario, a swarm of 20 robots are manually launched from abox, search for one rescuer within a distance of 550 m, and reconfigure so toestablish a reliable communication link between the rescuer and the launchinglocation where the base station is supposed to be. Robots cannot rely on GPSand vision to know their own position and that of other robots. All a robot knows,is whether there is another robot within a surrounding area with a radius of 100m, and how many robots away (hop count) it is from the base station and fromthe rescuer (if one has been found).Hand-coding a suitable set of control systems for the swarm is difficult because,since the behaviors of the robots are locally and mutually dependent and cannotuse an absolute referential system, small behavioral variations can have largeand hard-to-predict consequences in the swarm dynamics. Therefore, we

Figure 1. Artist’s view of a deployable swarm ofautonomous robots for ad hoc communication

networks in case of catastrophic events.

resorted to a process of artificial evolution to generate control systems thatcollectively achieve the goal scenario. Once a satisfactory control system wasfound, we reverse engineered the evolved controller into a control program thatcould be easily understood and modified according to the scenario constraints.(The use of a different, non-evolutionary, strategy based on ants is described inanother poster presentation [2]).Artificial evolution of collective control systems however is not yet wellunderstood and, in particular, it is not clear how swarm fitness should bedistributed among swarm members and how similar or different should thecontrol system of swarm members be. We thoroughly investigated these twoquestions with the help of a swarm of wheeled robots and obtained guidelines forevolving swarms of individuals thatmust cooperate to achieve acollective task [3].These guidelines were thenapplied to the swarm of flyingrobots in this scenario. We carriedout all evolutionary experiments insimulation. The control systems ofthe robots consisted of a feed-forward neural network that wasequal for all robots. The fitnessfunction was the percentage oftime a reliable communication wasmaintained by the swarm.Strategies found through evolution(figure 2), allowed for theestablishment and maintenance ofa communication link between thebase station and rescuer.The trajectories of evolved control systems were analyzed and translated intotwo simple control principles. These rules were then implemented in thesimulated swarm and its performance measured under various conditions. Giventhese promising results, some functionalities of the hardware, such as the turningradius of the robot, have been matched to the evolved control rules in order toensure their porting from simulation to the physical robots.

References

[1] Leven, S. Zuffery, J-C. and Floreano, D. (2007) A Simple and Robust Fixed-WingPlatform for Outdoor Flying Robot Experiments. In this volume.[2] Hauert, S., Zuffery, J-C. and Floreano, D. (2007) Pheromone Based Swarming forPosition-less MAVs. In this volume.[3] Floreano, D., Mitri, S., Magnenat, S. and Keller, L. (2007) Evolutionary Conditionsfor the Emergence of Communication in Robots. Current Biology, 17, 514-519.

Figure 2. Flight trajectories of an evolvedswarm that found a rescuer and established a

communication link with the base station.