Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Abstract

This paper presents an energy and perception aware framework for path planning and navigation of unmanned aerial vehicles (UAVs) in GNSS-denied and spatiotemporal wind environments. The proposed framework mainly consists of the global and local path planning methods that respectively consider the energy consumption of an UAV and perception quality of a light detection and ranging (LiDAR) sensor mounted on the UAV. The energy consumption is estimated based on the aerodynamic model that calculates drag and lift forces on the UAV. The global planner then uses the total energy consumption in the spatiotemporal wind as the cost function to find an energy-efficient path as a set of waypoints. The local path planning navigates the UAV between the waypoints with maintaining the perception quality. The perception quality is quantified based on how well the LiDAR sensor scans feature points around the UAV that highly correlates with the navigation accuracy. Numerical simulation study for each of the global and local path planners validates their usefulness. Further, the overall framework is entirely verified in a long-range flight scenario of the photorealistic environments developed in the Gazebo simulation.