Energy-Minimization Path Planning and Control of Unmanned Aerial Systems for Advanced Air Mobility

Battery limitations continue to be a bottleneck for mass-adoption of electric drones within logistics applications, such as short-haul parcel or medical supply delivery. Therefore, it is becoming progressively more important to utilize the available energy in a most efficient manner. Energy optimization research provides us with increasingly accurate models for energy consumption estimation during drone flight, but there is scant research pertaining to real-time trajectory adjustment to ensure minimum energy usage. Additionally, few governing models take into account energy demands due to landing and take-off. A novel approach is introduced that uses a PID controller combined with fundamental energy equations that tracks real-time energy optimization along a predetermined path under parameter uncertainty. It is demonstrated that the controller effectively and accurately converges to the most optimal velocity with respect to the energy per meter (EPM) metric. Additionally, path optimization using Model Predictive Control is introduced within landing and take-off stages to further enhance the effectiveness of the proposed approach.