This paper describes the problem of offline autonomous mobile robot path planning, which is consist of generating optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An improved algorithm for solving the problem of path planning using Artificial Bee Colony algorithm is presented. This natureinspired metaheuristic algorithm, which imitates the foraging behavior of bees around their hive, is used to find the optimal path from a starting point to a target point. The proposed algorithm is demonstrated by simulations in three different environments. A comparative study is evaluated between the developed algorithm, the original ABC and other two state-ofthe-art algorithms. This study shows that the proposed method is effective and gets trajectories with satisfactory results. General TermsNature-inspired algorithm, Heuristic algorithms

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.