Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Motion Planning based on Nonlinear Model Predictive Control with
  Non-Euclidean Rotation Groups   This paper proposes a novel online motion planning approach to robot
navigation based on nonlinear model predictive control. Common approaches rely
on pure Euclidean optimization parameters. In robot navigation, however, state
spaces often include rotational components which span over non-Euclidean
rotation groups. The proposed approach applies nonlinear increment and
difference operators in the entire optimization scheme to explicitly consider
these groups. Realizations include but are not limited to quadratic form and
time-optimal objectives. A complex parking scenario for the kinematic bicycle
model demonstrates the effectiveness and practical relevance of the approach.
In case of simpler robots (e.g. differential drive), a comparative analysis in
a hierarchical planning setting reveals comparable computation times and
performance. The approach is available in a modular and highly configurable
open-source C++ software framework.

8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga