The robotic navigation includes path planning as a desperate characteristic where the robot tries to identify the optimal paths for traversing in complex environments. This paper presents an influential algorithm motivated by ants’ nature and characteristics called as ant colony optimization (ACO). The algorithm depends on the foraging behavior of ants. This proposed technique renovates the presented environment into a graphical representation where locations are represented as nodes and paths are represented by edges. The algorithm inspired by the ACO is used to pretend the cooperative intelligence of colonies of ants, where artificial “ants” move on the edges of the graph and lay the pheromone trails. The pheromone trails guide the subsequent traversing ants to move in the direction of higher pheromone concentrations which will ultimately lead and converge as optimal solutions. To improve the performance of algorithm, the incorporation of heuristic information can be thought of as additional domain-specific knowledge. The paper explores the parameter’s initialization, quantity of ants, evaporation rate of the pheromone, and the pheromone’s influence along with heuristics. The steps in the iterative process includes producing solutions, updating the levels of the pheromone, and evaporation which eventually lead to the detection of an optimal solution path. The effectiveness of the proposed technique is shown through simulations and experimental results, exhibiting its flexibility to enormous environments and capability to deal with dynamic obstacles. As the need of competent navigation systems are growing day by day, the incorporation of ACO in path planning of the robots stand out as one of the promising and effectual policy.