Redirected walking (RDW) enables users to walk naturally within a virtual environment that is larger than the physical environment. Recently, several artificial potential field (APF) and alignment-based redirected controllers have been developed and have been demonstrated to significantly outperform conventional controllers. APF Steer-to-Gradient (APF-S2G) and APF Redirected Walking (APF-RDW) utilize the negative gradient and the total force vector, respectively, which are localized to the user's position. These vectors usually point towards the opposite wall when the user is in corridors, resulting in frequent resets within those regions. This paper introduces the APF Steer-to-Target (APF-S2T), a redirected controller that first finds the target sample point with the lowest score in the user's walkable area in both physical and virtual environments. The score of a sample point is determined by the APF value at the point and the distance from the user's position. The direction from the user's position to the target point is then used as the steering direction for setting RDW gains. We conducted a simulation-based evaluation to compare APF-S2T, APF-S2G, APF-RDW, Visibility Polygon-based alignment (Vis.-Poly.) and Alignment-Optimized controllers in terms of the number of resets and the average distance between resets. The results indicated that APF-S2T significantly outperformed the state-of-the-art controllers.