Akademik Veri Yönetim Sistemi
ICNES 2025, Siirt, Türkiye, 20 - 21 Aralık 2025, ss.11, (Özet Bildiri)
An enhanced A*, based path planning
method is described in this paper to make navigation safe, efficient, and
dependable for unmanned ground vehicles (UGVs) in complicated, unstructured,
and dynamically challenging off, road environments. A* algorithms of the
conventional type are usually effective in structured and static scenarios;
however, they have limitations such as restricted motion flexibility and lack
of environmental awareness in irregular terrains with elevation changes,
surface properties varying in different areas, and risk regions. The major
features of the proposed approach are the three, level environmental
representation, the extended 24, neighborhood search strategy, and task,
prioritized cost functions that, among other things, consider time efficiency,
operational safety, and path smoothness within a single planning framework.
A grid, based map that combines
terrain traversability information, elevation data from a digital elevation
model, and risk regions resulting from terrain features is used to model the
environment. Such a multi, dimensional representation allows the planner to
weigh different route options more thoroughly by considering not only the
feasibility of the traversal but also the safety aspects and the smoothness of
the motion. By using a 24, neighborhood search strategy, the search space is
considerably larger than that of conventional 8, connected grids thus the
resulting trajectories are smoother with less abrupt heading changes and better
maneuverability.
The method put forward is executed and
verified through a wide range of simulation experiments carried out in the
MATLAB setting and designed to test different terrain configurations and
operational constraints. The outcomes reveal that the enhanced A* technique is
a good fit for the problem at hand since it is able to provide paths that are
smoother, more feasible, and adapted to the terrain than those obtained by
traditional grid, based planning methods. The results of this research serve as
a proof, of, concept for the proposed method as a potentially effective way to
navigate unmanned vehicles in complex and challenging off, road terrain
scenarios.