  This manuscript explores the complexities of multi-objective path planning,
aiming to optimize routes against a backdrop of conflicting performance
criteria. The study integrates the cell mapping approach as its foundational
concept. A two-pronged search strategy is introduced; initially, the cell
mapping technique is utilized to develop a comprehensive database, encompassing
all cells within the specified area. This database records the performance
metrics for the most efficient routes from each cell to the designated target.
The second phase involves analyzing this database to pinpoint the extent and
count of all Pareto optimal routes from a selected starting cell to the target.
This analysis contributes to solving the overarching multi-objective
optimization challenge inherent in path planning. To validate this approach,
case studies are included, and the results are benchmarked against the
well-established multi-objective A* (MOA*) method. The study discovers that
while the cell mapping method achieves similar outcomes to the MOA* method for
routes originating from a single point, it demonstrates superior computational
benefits, particularly when the starting and ending points are in separate,
non-overlapping areas.
