Abstract: This paper proposes a method to find an optimal local path based on an improved version of the MAX-MIN Ant Colony Optimization (ACO) algorithm in dynamic robot path environments. It uses the grid method to decompose two-dimensional space to build class nodes that contain the information of the space environment. The proposed improvement of MAX�MIN ACO algorithm occurs in the stage of mixing pheromone trail updating with D* algorithm strategies to construct the consequence modified (deposited) pheromone trail update in each iteration. Thus the robot (ant) analyses the environment from the goal node (food) and computes the cost (pheromone deposition) for all the nodes to the start node (nest). The robot uses tour construction probabilities to choose the best solution to move it from the start node through dynamic environment which contains dynamic obstacle moving in free space by finding and displaying the optimal path. Some experimental results that are simulated in different dynamic environments, show that the robot reaches its target without colliding obstacles and finds the optimal local path with minimum iterations, minimum total path cost and minimum time occupy.