Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Abstract

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

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

“…Comparison with Other Path Planning Algorithms In this subsection, the performance of the proposed path planning algorithm using a hybrid PSO-MFB algorithm is compared with the works of [33][34][35]. The first case study involved an environment also used in these works, which consists of four static obstacles.…”

“…The first case study involved an environment also used in these works, which consists of four static obstacles. The optimization techniques used to obtain the best path were the Direct Artificial Bee Colony (DABC) and Minimum Angle Artificial Bee Colony (MAABC) algorithms in [33] while the work in [34] included GA and Bacterial Colony (BC) algorithms. The proposed Hybrid PSO-MFB algorithm was applied in the same environment as shown in Fig.…”

“…The environment in [35], which involved six static obstacles, was used in the second case study for comparison. The optimization technique used to obtain the shortest path was a standard ABC in [35], and DABC and MAABC in [33]. The proposed Hybrid PSO-MFB algorithm was applied to the same environment, as shown in The best path obtained using the proposed Hybrid PSO-MFB algorithm was 14.6384 m, compared to 14.7422 m and 14.7163 m using the DABC, MAABC algorithms, respectively.…”

Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

“…Generally, there are many paths for robot to reach the target, but in fact, the best path is selected according to some guideline. These guidelines are: shortest distance, least energy consuming or shortest time with the ce is the most adopted criteria [1]. Path planning can be seen as an optimization problem since its purpose is to search for a path under certain constraints such as the given environment with collision-free everal decades, research on optimization algorithms has covered a wide area of researchers' attention.…”

“…In BFO, the position updating, which simulates the chemotaxis procedure is used in Eq. (1). θ represents the position of the ith bacterium in the jth chemotaxis step, C i is the step length during the jith chemotaxis and∅ i is a unit vector which stands for the swimming direction after a tumble.…”