# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 2 State of the Art  
Several authors in the field of swarm robotics develop behaviours that are energy efficient with respect to a specific energy model. These models are often idealised and do not accurately represent the energy consumption of a real robot. [PERSON] et al. [15] use the travelled distance to approximate energy consumption of a robot. [PERSON] et al. [13] assume that energy is spent for certain manoeuvres like starting and landing and that mission time is a key factor for the energy consumption of quadcopters. [PERSON] et al. [14] only approximate the additional energy spend in the presence of external influences like wind and assume a fixed baseline consumption. [PERSON] et al. [16] present an energy model which is based on the analysis of empirical flight data only. The authors model the quadcopter as a fixed-weight object directly investing energy to move around in the air. The modelled flight behaviours are divided into three categories: Hovering, Horizontal Flight and Vertical Flight. In this model, the consumed energy of the quadcopter for hovering depends on the hover time only. The energy for horizontal and vertical movements is solely based on the flight angle and distance of the manoeuvre. The z-score of the validation flights showed results mostly lower than 1.0. This indicates strong similarity to the energy usage of the real copter. However, the energy model is very specific for the presented type of quadcopter. In consequence, the used methodology and the results cannot be transferred easily to other quadcopters. The work of [PERSON] and [PERSON] [17] presents an empirical energy model dependent on speed and acceleration of the quadcopter to be used for coverage path planning. The energy model was validated through autonomous flights of a quadcopter through a set of waypoints over a random area at a constant height. For this test mission, the real measured energy was compared to the estimatedenergy through the model resulting in a relative error of 0.02. The model is tightly coupled with the intended use case of the paper. The considered manoeuvres are limited to the ones created by the coverage path planning, which contains many 90\({}^{\circ}\) angle turns. These movements are very specific movements for the used coverage path planning algorithm. The evaluation of the energy model is very limited, as only a single validation flight was conducted. In the paper of [PERSON] et al. (2018) an endurance model for the ARDrone 2.0 is developed. The model combines a battery model of a Lithium-Polymer Battery (LiPo) battery and an energy consumption model for the quadcopter, based on momentum theory. In various test flights, the authors observed a similarity between the required power for manoeuvres and the power needed for hovering. Therefore, the authors only consider the hovering power in their final model. Two models of different complexity were tested. The most complete power model was tested by flying 27 different missions. The results showed a standard deviation of the estimated power of ~5% of the flight power. Tests with an identically constructed quadcopter resulted in model parameters which differed by 100%. Therefore, the authors considered this model to be sensitive to the specifics of the quadcopter used. A simpler model achieved a standard deviation of 10% and showed more robustness regarding the specifics of the used quadcopter. A unique approach to reduce energy consumption is presented by [PERSON] et al. (2019) and [PERSON] et al. (2018). The authors show a mechanism to attach a quadcopter to the ceiling to disable the motors while still providing birds-eye view. Additionally, an endurance estimation model was developed for this set-up. Using the main assumption that the quadcopters thrust equals its weight while flying, the relationship between power consumption and thrust based on the used motor-propeller combination is characterised via static thrust measurements with a custom test rig.