For some tasks, the use of more than one robot may improve the speed, reliability, or flexibility of completion, but many other tasks can be completed only by multiple robots. This paper investigates controller design using multi-objective genetic programming for a multi-robot system to solve a highly constrained problem, where multiple unmanned aerial vehicles (UAVs) must monitor targets spread sparsely throughout a large area. UAVs have a small communication range, sensor information is limited and noisy, monitoring a target takes an indefinite amount of time, and evolved controllers must continue to perform well even as the number of UAVs and targets changes. An evolved task selection controller dynamically chooses a target for the UAV based on sensor information and communication. Controllers evolved using several communication schemes were compared in simulation on problem scenarios of varying size, and the results suggest that this approach can evolve effective controllers if communication is limited to the nearest other UAV.

While evolving evolutionary robotics controllers for real vehicles is an active area of research, most research robots do not require any assurance prior to operation that an evolved controller will not damage the vehicle. For controllers evolved in simulation where testing a poorly performing controller might damage the vehicle, thorough testing in simulation - subject to multiple sources of sensor and state noise - is required. Evolved controllers must be robust to noise in the environment in order to operate the vehicle safely. We have evolved navigation controllers for unmanned aerial vehicles in simulation using multi-objective genetic programming, and in order to choose the best evolved controller and to assure that this controller will perform well under a variety of environmental conditions, we have performed a series of robustness tests. The results show that our best evolved controller outperforms two hand-designed controllers and is robust to many sources of noise.

Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.

Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multiobjective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multiobjective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs.

Smart Sensor Networks (S-nets) are groups of stationary agents (S-elements) which provide distributed sensing, computation, and communication in an environment. In order to integrate information from individual agents and to efficiently transmit this information to other agents, these devices must be able to create local groups (S-clusters). A leadership protocol that creates static clusters has been previously proposed. Here, we further develop this protocol to allow for dynamic cluster updating. This accommodates on-the-fly network re-organization in response to environmental disturbances or the gain or loss of S-elements. We outline an informal argument for the correctness of this revised protocol. We describe our embedded system implementation of the leadership protocol in simulation and using a colony of robots. Finally, we present results demonstrating both implementations.

This paper describes the development and testing of a new evolutionary robotics research test bed. The test bed consists of a colony of small computationally powerful mobile robots that use evolved neural network controllers and vision based sensors to generate team game-playing behaviors. The vision based sensors function by converting video images into range and object color data. Large evolvable neural network controllers use these sensor data to control mobile robots. The networks require 150 individual input connections to accommodate the processed video sensor data. Using evolutionary computing methods, the neural network based controllers were evolved to play the competitive team game Capture the Flag with teams of mobile robots. Neural controllers were evolved in simulation and transferred to real robots for physical verification. Sensor signals in the simulated environment are formatted to duplicate the processed real video sensor values rather than the raw video images. Robot controllers receive sensor signals and send actuator commands of the same format, whether they are driving physical robots in a real environment or simulated robots agents in an artificial environment. Evolved neural controllers can be transferred directly to the real mobile robots for testing and evaluation. Experimental results generated with this new evolutionary robotics research test bed are presented.