The path-planning algorithm aims to find the optimal path between the starting and goal points without collision. One of the most popular algorithms is the optimized Rapidly exploring Random Tree (RRT*). The strength of RRT* algorithm is the collision-free path. It is the main reason why RRT-based algorithms are used in path planning for mobile robots. The RRT* algorithm generally creates the node for randomly making a tree branch to reach the goal point. The weakness of the RRT* algorithm is in the random process when the randomized nodes fall into the obstacle regions. The proposed algorithm generates a new random environment by removing the obstacle regions from the global environment. The objective is to minimize the number of unusable nodes from the randomizing process. The results show better performance in computational time and overall path-planning length. Bacterial mutation and local search algorithms are combined at post-processing to get a better path length and reduce the number of nodes. The proposed algorithm is tested in simulation.

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