Abstract
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.
Highlights
The field robot path planning was launched at the middle of the 1960s
Path planning can be seen as an optimization problem since its purpose is to search for a path with shortest distance under certain constraints such as the given environment with collision-free motion [2]
Optimization methods and algorithms can be classified in many types, but the simplest way is to look at the nature of the algorithm, and this grouped them into deterministic and stochastic [3, 4] where deterministic techniques depend on the mathematical nature of the problem, while stochastic techniques do not depend on the mathematical properties of a given function and are more appropriate for finding the global optimal solutions for any type of objective function
Summary
The field robot path planning was launched at the middle of the 1960s. Robot path planning is an important problem in navigation of mobile robots. Stochastic techniques that mimic the behaviors of certain animals or insects (birds, ants, bees, flies and even germs!) and called Nature-Inspired Algorithms have been developed since 1980s. These nature-inspired paradigms have already come to be widely used in many areas in engineering fields [5, 6]. M. Passino [10] in 2002.BFO is a simple but powerfulbioinspired optimization technique uses the analogy of swarming principles and social behavior in nature - swarm intelligence- and it have been adopted to solve a variety of engineering and mobile robotics problems, including path planning problem [11].
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