Biblio

We use potential fields tuned by genetic algorithms to dynamically reconFigure unmanned aerial vehicles networks to serve user bandwidth needs. Such flying network base stations have applications in the many domains needing quick temporary networked communications capabilities such as search and rescue in remote areas and security and defense in overwatch and scouting. Starting with an initial deployment that covers an area and discovers how users are distributed across this area of interest, tuned potential fields specify subsequent movement. A genetic algorithm tunes potential field parameters to reposition UAVs to create and maintain a mesh network that maximizes user bandwidth coverage and network lifetime. Results show that our evolutionary adaptive network deployment algorithm outperforms the current state of the art by better repositioning the unmanned aerial vehicles to provide longer coverage lifetimes while serving bandwidth requirements. The parameters found by the genetic algorithm on four training scenarios with different user distributions lead to better performance than achieved by the state of the art. Furthermore, these parameters also lead to superior performance in three never before seen scenarios indicating that our algorithm finds parameter values that generalize to new scenarios with different user distributions.

Throughout the last few decades, a breakthrough took place in the field of autonomous robotics. They have been introduced to perform dangerous, dirty, difficult, and dull tasks, to serve the community. They have been also used to address health-care related tasks, such as enhancing the surgical skills of the surgeons and enabling surgeries in remote areas. This may help to perform operations in remote areas efficiently and in timely manner, with or without human intervention. One of the main advantages is that robots are not affected with human-related problems such as: fatigue or momentary lapses of attention. Thus, they can perform repeated and tedious operations. In this paper, we propose a framework to establish trust in autonomous medical robots based on mutual understanding and transparency in decision making.