Abstract
Increasing the flight endurance of unmanned aerial vehicles (UAVs) has received attention recently. To solve this problem, two research topics have generally appeared: Shortest-path planning (SPP) and remaining-flying-range estimation. In this work, energy-efficient path planning by considering the distance between waypoint nodes, the minimum and maximum speed of the UAV, the weight of the UAV, and the angle between two intersecting edges is proposed. The performances of energy-efficient path planning (EEPP) and generic shortest-path planning are compared using extended-Kalman-filter-based state-of-charge and state-of-power estimation. Using this path-planning tool and considering energy consumption during flight operation, two different path plans can be obtained and compared in advance so that the operator can decide which path to choose by consulting a comparison chart. According to the experimental results, the EEPP algorithm results in 0.96% of improved SOC leftover and 11.03 ( W ) of lowered SOP compared to the SPP algorithm.
Highlights
As unmanned aerial vehicle (UAV) technology matures, the longtime flight endurance capability of unmanned aerial vehicles (UAVs) is gaining attention [1,2]
With the achieved SOC value, one can determine whether the efficient path planning (EEPP) algorithm results in an energy-efficient path
According to the experimental results, the EEPP algorithm results in an energy-efficient path
Summary
As unmanned aerial vehicle (UAV) technology matures, the longtime flight endurance capability of UAVs is gaining attention [1,2]. Accurate energy leftover monitoring (state of charge (SOC) estimation in the EV industry), aims to extend the driving range, and it is related to shortest-path planning (SPP), which is essential to saving energy. Unlike the previously listed works, this study contributes to the energy-efficient path planning (EEPP) of the UAV to increase the flight endurance compared with the general SPP. Either flight simulation with equivalent circuit model (ECM) of the Li-Ion battery pack or the flight experiment with a real Li-Ion battery pack is performed using the previously obtained energy-efficient path and gives out the voltage and current profiles of each cell. The flow of this paper is as follows: In Section 2, the overall mission hierarchy, including four subparts (path planning, flight simulation, flight experiment, and battery state estimation), is described.
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