Policy Learning for Autonomous Feature Tracking

TY - JOUR

T1 - Policy Learning for Autonomous Feature Tracking

AU - Magazzeni, Daniele

AU - Py, Frédéric

AU - Fox, Maria

AU - Long, Derek

AU - Rajan, Kanna

PY - 2014/6

Y1 - 2014/6

N2 - We consider the problem of tracing the structure of oceanological features using autonomous underwater vehicles (AUVs). Solving this problem requires the construction of a control strategy that will determine the actions for the AUV based on the current state, as measured by on-board sensors and the historic trajectory (including sensed data) of the AUV. We approach this task by applying plan-based policy-learning, in which a large set of sampled problems are solved using planning and then, from the resulting plans a decision-tree is learned, using an established machine-learning algorithm, which forms the resulting policy. We evaluate our approach in simulation and report on sea trials of a prototype of a learned policy. We indicate some of the lessons learned from this deployed system and further evaluate an extended policy in simulation.

AB - We consider the problem of tracing the structure of oceanological features using autonomous underwater vehicles (AUVs). Solving this problem requires the construction of a control strategy that will determine the actions for the AUV based on the current state, as measured by on-board sensors and the historic trajectory (including sensed data) of the AUV. We approach this task by applying plan-based policy-learning, in which a large set of sampled problems are solved using planning and then, from the resulting plans a decision-tree is learned, using an established machine-learning algorithm, which forms the resulting policy. We evaluate our approach in simulation and report on sea trials of a prototype of a learned policy. We indicate some of the lessons learned from this deployed system and further evaluate an extended policy in simulation.

KW - Policy-based control

KW - Planning-based policy-learning

KW - Autonomous underwater vehicles

U2 - 10.1007/s10514-013-9375-7

DO - 10.1007/s10514-013-9375-7

M3 - Article

SN - 0929-5593

VL - 37

SP - 47

EP - 69

JO - Autonomous Robots

JF - Autonomous Robots

IS - 1

ER -