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
In this paper, design of proportional- derivative (PD) controller, pseudo-derivative-feedback (PDF) controller and PDF with feedforward (PDFF) controller for magnetic suspending system have been presented. Tuning of the above controllers is achieved based on Bat algorithm (BA). BA is a recent bio-inspired optimization method for solving global optimization problems, which mimic the behavior of micro-bats. The weak point of the standard BA is the exploration ability due to directional echolocation and the difficulty in escaping from local optimum. The new improved BA enhances the convergence rate while obtaining optimal solution by introducing three adaptations namely modified frequency factor, adding inertia weight and modified local search. The feasibility of the proposed algorithm is examined by applied to several benchmark problems that are adopted from literature. The results of IBA are compared with the results collected from standard BA and the well-known particle swarm optimization (PSO) algorithm. The simulation results show that the IBA has a higher accuracy and searching speed than the approaches considered. Finally, the tuning of the three controlling schemes using the proposed algorithm, standard BA and PSO algorithms reveals that IBA has a higher performance compared with the other optimization 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.
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