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.

Autonomous Hybrid GroundAerial Mobility in Unknown Environments Hybrid ground and aerial vehicles can possess distinct advantages over groundonly or flightonly designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid groundair vehicles. Our contribution is threefold: 1) We develop a control scheme for the control of passive twowheeled hybrid groundaerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only1.1Video at https:youtu.benlGfYehTLpg