TY - JOUR
T1 - Graph-based motor primitive generation framework
T2 - UAV motor primitives by demonstration-based learning
AU - Sung, Yunsick
AU - Kwak, Jeonghoon
AU - Park, Jong Hyuk
N1 - Publisher Copyright:
© 2015, Sung et al.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Unmanned aerial vehicles (UAVs) have many potential applications, such as delivery, leisure, and surveillance. To enable these applications, making the UAVs fly autonomously is the key issue, and requires defining UAV motor primitives. Diverse attempts have been made to automatically generate motor primitives for UAVs and robots. However, given that UAVs usually do not fly as expected because of external environmental factors, a novel approach for UAVs needs to be designed. This paper proposes a demonstration-based method that generates a graph-based motor primitive. In the experiment, an AR.Drone 2.0 was utilized. By controlling the AR.Drone 2.0, four motor primitives are generated and combined as a graph-based motor primitive. The generated motor primitives can be performed by a planner or a learner, such as a hierarchical task network or Q-learning. By defining the executable conditions of the motor primitives based on measured properties, the movements of the graph-based motor primitive can be chosen depending on changes in the indoor environment.
AB - Unmanned aerial vehicles (UAVs) have many potential applications, such as delivery, leisure, and surveillance. To enable these applications, making the UAVs fly autonomously is the key issue, and requires defining UAV motor primitives. Diverse attempts have been made to automatically generate motor primitives for UAVs and robots. However, given that UAVs usually do not fly as expected because of external environmental factors, a novel approach for UAVs needs to be designed. This paper proposes a demonstration-based method that generates a graph-based motor primitive. In the experiment, an AR.Drone 2.0 was utilized. By controlling the AR.Drone 2.0, four motor primitives are generated and combined as a graph-based motor primitive. The generated motor primitives can be performed by a planner or a learner, such as a hierarchical task network or Q-learning. By defining the executable conditions of the motor primitives based on measured properties, the movements of the graph-based motor primitive can be chosen depending on changes in the indoor environment.
UR - http://www.scopus.com/inward/record.url?scp=84950243049&partnerID=8YFLogxK
U2 - 10.1186/s13673-015-0051-0
DO - 10.1186/s13673-015-0051-0
M3 - Article
AN - SCOPUS:84950243049
SN - 2192-1962
VL - 5
SP - 1
EP - 9
JO - Human-centric Computing and Information Sciences
JF - Human-centric Computing and Information Sciences
IS - 1
M1 - 35
ER -