Kirill Krinkin

Unmanned Aerial Vehicles can act as relays in areas with limited infrastructure to support car-to-car communications. Prior studies on UAV-to-car communications showed that the irregularity of the terrains has a significant impact on link quality. Thus, in this paper, we propose a positioning technique that relies on Particle Swarm Optimization to optimize the positioning of a UAV in the vehicular environment by considering the irregularities of the terrains that might hinder Line-of-Sight conditions. The proposed technique takes into account the path loss caused by the terrains.

Modern SLAM (Simultaneous Localization and Mapping) algorithms launched on a moving agent are bounded with its computation resources. The consistent way out is to add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. This paper presents the state of art in area of Multi-agent SLAM algorithms and describes problems that are faced in front of a developer of such approach. The outstanding problem of Multiagent SLAM - merging of maps built by separate agent during algorithm is also considered in this paper. Moreover the algorithm that extends laser 2D single hypothesis SLAM for multiple agents is introduced with evaluation of its performance.

An autonomous self driving platform receives information about environment using only its onboard sensors. And it seems obvious that using several sensors could provide more certain information with reduced measurement error. But a general question is how to fuse measurements from different kinds of sensors (like a camera and an accelerometer) to get refined data about a platform or world state. This paper presents a theory based on groups that proves a possibility of correctness of error extraction from a moving model. And there are results of application this theory on fusing measurements from two sensors: odometer and scan matcher