A blimp-type unmanned aerial vehicle (BUAV) maintains its longitudinal motion using buoyancy provided by the air around it. This means the density of a BUAV equals that of the surrounding air. Because of this, the motion of a BUAV is seriously affected by flow disturbances, whose distribution is usually non-uniform and unknown. In addition, the inertia in the heading motion is very large. There is also a strict limitation on the weight of equipment in a BUAV, so most BUAVs are so-called under-actuated robots. From this situation, it can be said that the motion planning of the BUAV considering the stochastic property of the disturbance is needed for obstacle avoidance. In this paper, we propose an approach to the motion planning of a BUAV via the application of Markov decision process (MDP). The proposed approach consists of a method to prepare a discrete MDP model of the BUAV motion and a method to maintain the effect of the unknown wind on the BUAV’s motion. A dynamical simulation of a BUAV in an environment with wind disturbance shows high performance of the proposed method.
Motion representations for the Lafferriere-Sussmann algorithm for nilpotent control systems