EAOA: EnergyAware GridBased 3DObstacle Avoidance in Coverage Path Planning for UAVs The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAVxe2x80x99s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAVxe2x80x99s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energyaware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area topview. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

Air To Ground Collaboration For Energyefficient Path Planning For Ground Robots We study a fundamental motion planning problem of navigating a ground robot to a goal position with minimum energy consumption. Most existing solutions for this problem require an energy consumption model as a function of the environment and the robot motion. Obtaining such models is difficult which prevents the practical applicability of path planning algorithms for energy optimization. To address this issue, we present a new approach based on the assumption that the energy consumption for the ground robot is correlated with ground appearance. The first main contribution of this paper is the validation of the ground appearance assumption by experiments using actual energy consumption data obtained by ground robots. We then show how aerial images collected by an unmanned aerial vehicle can be used to generate the energy cost map of a given environment, which can further be used for planning energyefficient paths for ground robots.