  For rapid growth in technology and automation, human tasks are being taken
over by robots as robots have proven to be better with both speed and
precision. One of the major and widespread usages of these robots is in the
industrial businesses, where they are employed to carry massive loads in and
around work areas. As these working environments might not be completely
localized and could be dynamically changing, new approaches must be evaluated
to guarantee a crash-free way of performing duties. This paper presents a new
and efficient fusion algorithm for solving the path planning problem in a
custom 2D environment. This fusion algorithm integrates an improved and
optimized version of both, A* algorithm and the Artificial potential field
method. Firstly, an initial or preliminary path is planned in the environmental
model by adopting the A* algorithm. The heuristic function of this A* algorithm
is optimized and improved according to the environmental model. This is
followed by selecting and saving the key nodes in the initial path. Lastly, on
the basis of these saved key nodes, path smoothing is done by artificial
potential field method. Our simulation results carried out using Python viz.
libraries indicate that the new fusion algorithm is feasible and superior in
smoothness performance and can satisfy as a time-efficient and cheaper
alternative to conventional A* strategies of path planning.
