Pathfinding algorithms are fundamental to autonomous navigation, traditionally computing optimal routes based on defined start and goal locations using heuristic cost functions. While effective, these algorithms become computationally intensive in dynamic environments due to the need for exhaustive search. Gradient-based optimization methods, such as Broyden-Fletcher-Goldfarb-Shanno (BFGS), offer a reactive alternative by making local decisions based on the current position; however, they are susceptible to local minima and increased computational burden in complex cost landscapes, particularly when second-order derivative information is required. This paper proposes an enhanced \(A^*\) algorithm through the introduction of a custom heuristic function designed to reduce computational load and facilitate reactive navigation. The proposed heuristic, along with three well-established heuristics, is evaluated against an augmented BFGS approach that integrates a repulsive force field to improve obstacle avoidance. Performance metrics include path length and path behavior near obstacles. Experimental results demonstrate that the \(A^*\) algorithm with the proposed heuristic achieves obstacle avoidance performance comparable to BFGS, outperforming traditional heuristics. These findings suggest the potential for the enhanced \(A^*\) to support reactive robotic navigation, motivating further exploration towards hybrid strategies that leverage the strengths of both global search and local optimization methods.