# Development of Path Planning Tool for Unmanned System Considering Energy Consumption
## 1 Introduction  
As unmanned aerial vehicle (UAV) technology matures, the longtime flight endurance capability of UAVs is gaining attention [1, 2]. Because the energy density (W/kg) of the battery pack (in particular, Li-ion) is closely correlated with the overall flight time of the UAV, it is recommended to choose cells possessing as high an energy density as possible [3, 4]. However, in contrast to electric vehicles (EVs), the operation times of electric aircraft, including the UAV, drastically decrease as the system weights increase. Hence, technology development for increasing UAV flight time has been delayed for a long time. Lately, the EV industry has been growing quickly, and automotive industry researchers have focused their attention on accurately predicting the energy leftover so that the driving range can be extended. 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. Both accurate SOC estimation and SPP should ultimately enhance the fuel economy of EVs, and the same philosophy applies to UAVs. There have been numerous studies regarding path planning. [PERSON] et al. proposed a method to achieve an effective delivery route for trucks carrying delivery drones with moving shift-weights-based K-means clustering and traveling-salesman-problem modeling [5]. [PERSON] et al. presented adaptive path planning for multiple UAVs using evolutionary computation combined with market-based cooperation strategies [6]. Further, there has been research regarding the accurate estimation of the remaining driving or flying range. [PERSON] et al. suggested a model-based approach by combining unscented filtering and Markov chains to predict the remaining driving range (RDR) [7]. [PERSON] et al. proposed the efficient computation of RDR confidence level using a feature-based linear regression framework with a varyingprobabilistic attainability map in real-time [8]. [PERSON] et al. presented an interaction method between a driver and EV to estimate EV range using a regression-model-based data mining approach [9]. Unlike the previously listed works, this study contributes to the energy-efficient path planning (EEPP) of the UAV to ultimately increase the flight endurance compared with the general SPP. To verify the improved fuel efficiency, the concept of the extended Kalman filter (EKF)-based SOC estimation method is adopted. To mimic cell behavior, a physics-based equivalent circuit model (ECM) is used [10; 11; 12; 13]. The overall mission hierarchy to achieve an energy-efficient path proposed in this paper is as follows. First, with the given satellite map, the EEPP algorithm generates the most energy-efficient path with step-by-step processes; (1) obstacle detection, (2) extract obstacle, (3) obstacle separation, (4) add buffer zone, (5) Voronoi, (6) filter out useless candidates, (7) 1D optimal control, and (8) SPP or EEPP selection. Here, the format of the output path has both Euclidean and GPS coordinates. In contrast to the SPP algorithm, which uses Dijkstra algorithm, the EEPP algorithm uses three step-by-step processes; (1) find all trajectory candidates, (2) calculate total unit energy consumption (UEC) of all trajectory candidates, and (3) find a trajectory having the minimum UEC. Second, 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. Third, a battery state estimator uses the voltage and current profiles of cells to calculate the SOC. In particular, the battery state estimator uses the EKF to calculate the SOC. 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.