  Path planning is one of the most vital elements of mobile robotics. With a
priori knowledge of the environment, global path planning provides a
collision-free route through the workspace. The global path plan can be
calculated with a variety of informed search algorithms, most notably the A*
search method, guaranteed to deliver a complete and optimal solution that
minimizes the path cost. Path planning optimization typically looks to minimize
the distance traversed from start to goal, yet many mobile robot applications
call for additional path planning objectives, presenting a multiobjective
optimization (MOO) problem. Past studies have applied genetic algorithms to MOO
path planning problems, but these may have the disadvantages of computational
complexity and suboptimal solutions. Alternatively, the algorithm in this paper
approaches MOO path planning with the use of Pareto fronts, or finding
non-dominated solutions. The algorithm presented incorporates Pareto optimality
into every step of A* search, thus it is named A*-PO. Results of simulations
show A*-PO outperformed several variations of the standard A* algorithm for MOO
path planning. A planetary exploration rover case study was added to
demonstrate the viability of A*-PO in a real-world application.
