Obstacle Avoidance and Location Determination of an Unmanned Aerial Vehicle in a GPS-Denied, Hazard-Cluttered Indoor Environment.

2015 ◽  
Vol 3 (2) ◽  
pp. 75-99 ◽  
Author(s):  
Sarthak Kukreti ◽  
Anoop Sathyan
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Indoor Environment ◽  
Location Determination ◽  
Aerial Vehicle

Location Determination of an Unmanned Aerial Vehicle in a GPS-Denied, Hazard-Cluttered Indoor Environment

2015 ◽  
Author(s):  
Siddharth Sridhar ◽  
Anoop Sathyan ◽  
Sarthak Kukreti ◽  
Elad H. Kivelevitch
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Indoor Environment ◽  
Location Determination ◽  
Aerial Vehicle

Determination of Spring Barley Lodging Area with Help of Unmanned Aerial Vehicle

2021 ◽  
pp. 247-257
Author(s):  
Rashid K. Kurbanov ◽  
Natalia I. Zakharova
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spring Barley ◽  
Aerial Vehicle

Three-Dimensional Obstacle Avoidance of Blimp-Type Unmanned Aerial Vehicle Flying in Unknown and Non-Uniform Wind Disturbance

2007 ◽  
Vol 19 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Hiroshi Kawano ◽  
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Unmanned Aerial Vehicle ◽  
High Performance ◽  
Three Dimensional ◽  
Longitudinal Motion ◽  
Wind Disturbance ◽  
Flow Disturbances ◽  
Markov Decision ◽  
Aerial Vehicle

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.

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