AERO: Towards Energy-Efficient Autonomous Flight in MAVs Using Approximate Execution

Micro aerial vehicles (MAVs) are popular in many intelligent robot areas nowadays. As an battery-powered vehicle, the energy-efficiency of MAVs becomes the bottleneck for its wide adoption. This issue exacerbates for autonomous flight MAVs, since inefficient action executions may results in flight mission failure. In this work, we address the energy-inefficiency of deep Q-network (DQN) based autonomous flight of MAVs via approximate execution. We first investigate data sensing during flight process and observe two consecutive steps tend to perceive consistent data. We further analyze the decision making characteristics of DQN algorithm, and find the margins between maximum and the second largest Q-value in different steps are usually symmetrically distributed between action changes. Leveraging these two characteristics, we propose to Approximately Execute the autonomous flight pROcessing of MAVs for energy-efficiency improvement (AERO). AERO is composed of two techniques of AERO-S and AERO-AP. AERO-S uses the sensed data from the previous step to approximately infer the current sensing data. AERO-AP uses the symmetry of margins between the maximum and the second largest Q-value to skip the decision makings of some steps. Our evaluations show in environments with no obstacle, AERO achieves relative improvement of time (RIT) of 24.28% and relative improvement of energy (RIE) of 18.75% with no reduction of success rate. For environments with obstacles, AERO achieves 19.11% RIT and 12.72% RIE with no reduction of success rate.