鈴木 智

Recently, in order to carry out tasks efficiently such as infrastructure inspection and goods transportation, operations using multi-rotor Unmanned Aerial Vehicles (UAVs) in formation flight are often considered. One of the main issues in motion planning among multiple UAVs is collision avoidance. Model Predictive Control (MPC) is characterized by its ability to consider collision avoidance in the framework of constrained optimization. For this reason, there have been many studies on collision avoidance using MPC, but few studies take into account the uncertainty that occurs in real environments. On the other hand, Chance constrained MPC (CCMPC) is considered to be more robust in collision avoidance due to the consideration of uncertainty. However, the structure of the collision probability constraint equation to be introduced into the evaluation function of MPC has not been sufficiently studied. In this study, the structure of equations for incorporating probability constraints into the evaluation function is examined. Moreover, by quantitatively comparing the equations with the same structure with deterministic constraints introduced into the evaluation function, the difference in collision avoidance is clarified.