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Motion planning and collision avoidance functionality are crucial attributes to the successful deployment of mobile robots. This research analyzes some shortcomings of the canonical F2 method and then presents subgoal-guided force-field (SGF2) method to mitigate these drawbacks. In the proposed approach, a robot identifies openings in an environment in front of itself on the basis of sensor data. The midpoints of these openings are determined and selected as subgoal candidates. A cost function is then utilized to evaluate their suitability. One subgoal is then chosen and used by the F2 method to generate a steering force which will drive the robot to the subgoal. The subgoal is continuously updated from realtime sensor data until the global goal is reached. Simulations are carried out to demonstrate the effectiveness of the proposed approach.