In this research, we propose and implement obstacle detection and avoidance methods combining with a local incremental planning technique for navigating a car-like robot, equipped with GPS, IMU, and laser range finder sensors. To detect an obstacle, radial distance data from laser range finder is transformed to cartesian coordinate and mapped to a global occupancy-grid map. Thus, obstacles in real dynamic environment can be detected from a probability of occurrence in the global occupancy-grid map according to a specified threshold. Then, an integration of the local incremental planning and potential field techniques employ obstacles’ location from the global map to plan a trajectory from the robot current position to a desired target position. As a result, the vehicle can seek the target and avoid obstacles at the same time. Results of a vehicle simulation, developed in Matlab, reveal that the car-like robot can approach the target point and steer away from static obstacles according to an instantaneous virtual force acting on the vehicle center. Experimental results show that the proposed navigation method, developed in Matlab, can detect obstacle and avoid static obstacles and navigate to the desire target waypoints in the global occupancy map.