Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot

Abstract

Abstract-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 multiobjective 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.

Evolved Navigation Control for Unmanned Aerial Vehicles

“…The EvBot was only calibrated to turn at multiples of 5°: calibrating the EvBot to turn at angles smaller than 5° would have been unreliable due to the size of the EvBot and the characteristics of its motors. Controllers evolved with a less accurate sensor were not as well adapted as those from previous work using more accurate sensors; flight paths were much less smooth and required more turns (Barlow et al, 2005). An evolved controller was tested 10 times on an EvBot.…”

“…The evolved control system was only tested in simulation. Only recently has there been work on evolving GP controllers for fixed wing UAVs Barlow, 2004;Barlow et al, 2004;Barlow et al, 2005;Richards et al, 2005;Barlow & Oh, 2006). In evolutionary computation, incremental evolution (Harvey et al, 1994) is the process of evolving a population on a simple problem and then using the resulting evolved population as a seed to evolve a solution to a related problem of greater complexity.…”

“…Alternatively, controllers evolved in simulation do not always transfer well to real vehicles, since the simulation is never a perfect model of the real environment. Adding noise to the simulation (in the form of both sensor error and state error) may help controllers transfer well from simulation to real robots (Jakobi et al, 1995;Gomez and Miikkulainen, 2004;Barlow et al, 2005). This approach is usually evaluated by evolving a controller in a noisy simulation environment and then testing the controller on a real vehicle.…”

Robustness analysis of genetic programming controllers for unmanned aerial vehicles

“…Alternatively, controllers evolved in simulation do not always transfer well to real vehicles, since the simulation is never a perfect model of the real environment. Adding noise to the simulation (in the form of both sensor error and state error) may help controllers transfer well from simulation to real robots [2,5,7]. This approach is usually evaluated by evolving a controller in a noisy simulation environment and then testing the controller on a real vehicle.…”

“…As the experiments in [7] show, if the noise levels used in simulation are significantly different from those in the real world, there are no assurances that the evolved controller will perform as desired. If, however, a controller performs well when subjected to a wide range of sensor and state noise conditions in simulation, and the real environmental noise falls within the testing range, prior works suggest that the controller should also perform well on a real vehicle [2,5,7].…”