Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV
  with Limited Environment Knowledge   This paper presents our method for enabling a UAV quadrotor, equipped with a
monocular camera, to autonomously avoid collisions with obstacles in
unstructured and unknown indoor environments. When compared to obstacle
avoidance in ground vehicular robots, UAV navigation brings in additional
challenges because the UAV motion is no more constrained to a well-defined
indoor ground or street environment. Horizontal structures in indoor and
outdoor environments like decorative items, furnishings, ceiling fans,
sign-boards, tree branches etc., also become relevant obstacles unlike those
for ground vehicular robots. Thus, methods of obstacle avoidance developed for
ground robots are clearly inadequate for UAV navigation. Current control
methods using monocular images for UAV obstacle avoidance are heavily dependent
on environment information. These controllers do not fully retain and utilize
the extensively available information about the ambient environment for
decision making. We propose a deep reinforcement learning based method for UAV
obstacle avoidance (OA) and autonomous exploration which is capable of doing
exactly the same. The crucial idea in our method is the concept of partial
observability and how UAVs can retain relevant information about the
environment structure to make better future navigation decisions. Our OA
technique uses recurrent neural networks with temporal attention and provides
better results compared to prior works in terms of distance covered during
navigation without collisions. In addition, our technique has a high inference
rate (a key factor in robotic applications) and is energy-efficient as it
minimizes oscillatory motion of UAV and reduces power wastage.

10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an