# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
###### Abstract  
The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%. energy and energy-aware automation; aerial systems; perception and autonomy; model-building +
Footnote †: journal: Journal of the American Statistical Association  
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development of quadcopter systems. Extending the mission time by using larger capacity batteries is limited because larger batteries can block the airflow below the rotors and add weight. To compensate blocked airflow and additional weight the quadcopter needs more power. Mission planning and energy-aware decision-making may provide an alternative to enhance the energy efficiency of a single quadcopter, but are limited because the degrees of freedom in designing and optimising a mission are limited. Swarm robotics may mitigate this problem through the inherent cooperation. A swarm of multiple robots has more energy resources than a single robot thus energy becomes scalable through the number of robots. Furthermore, intelligent cooperation enables more efficient resource usage. Additionally, scaling the swarm can minimise mission time and enhances robustness in case of failures. Examples of such swarm robotic behaviours are the works of [PERSON] et al. [8], [PERSON] and [PERSON] [9] and [PERSON] et al. [10]. These algorithms focus on optimising mission time and scalability while solving a variety of different tasks [11; 12]. However, the critical problem of energy efficiency is seldom investigated. [PERSON] et al. [13] designed an energy-efficient modification of the Particle Swarm Optimisation (PSO) Algorithm, which can be applied to quadcopter search missions. [PERSON] et al. [14] worked on energy-efficient quadcopter movement under environmental influences (especially wind). [PERSON] et al. [15] proposed new metrics for optimisation algorithms, which take movement cost in the form of energy and time into account to evaluate existing optimisation algorithms regarding their applicability to swarm robotics. However, in all these works the energy model is very general, idealised and based on simple assumptions. Therefore, these energy models do not accurately represent the energy consumption of any real robot.