An Improved ABC Algorithm for Optimal Path Planning

International Journal of Computer Applications, 2014

The path planning of mobile robot is an important issue in the field of robotics. Many algorithms have been designed to solve the path planning problem, including classical as well as intelligent approaches. The main aim of path planning is to construct collision free path from a specified start position to the target position. Moreover, the path should be optimal in some context such as distance, time or processing. This paper presents an efficient algorithm which is a variation of artificial bee colony algorithm. The environment modelled is in the form of grid consisting of obstacles. The algorithm works on two problems-the first problem is to find collision free path in the presence of static obstacles and the second problem is to determine shortest collision free path in the presence of dynamic obstacles.

Engineering and Technology Journal, 2020

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal dista...

International Journal of Computer Applications, 2014

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.

Journal of Information and Telecommunication

Due to interference phenomena among unnatural dimensions of the motion robots' operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

Cogent Engineering

This paper presents a solution to plan a path using a new form of the Bees Algorithm for a 2-Wheeled Differential Drive mobile robot. This robot is used in an indoor environment. The environment consists of static and dynamic obstacles which are represented by a continuous configuration space as an occupancy mapbased. The proposed method is run in two respective stages. Firstly, the optimal path is obtained in the static environment using either the basic form or the new form of the Bees Algorithm. The initial population in the new form of the Bees Algorithm consists only of feasible paths. Secondly, this optimal path is updated online to avoid collision with dynamic obstacles. A modified form of the local search is used to avoid collision with dynamic obstacles and to maintain optimality of subpaths. A set of benchmark maps were used to simulate and evaluate the proposed algorithm. The results obtained were compared with those of the other algorithms for different sets of continuous maps. This comparison shows the superiority of the new form of the Bees Algorithm in solving this type of the problems. The proposed method was also tested using AmigoBot robot. In this experiment, the proposed

International Journal of Engineering & Technology, 2013

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

International Journal of Hybrid Information Technology, 2014

In Robot Colony System(RCS), based on the swarming nature of Ants, the path for the agent robots is designed to perform to search operation to find the shortest path from source to destination for collection of objects and go back to their hive-like home node. The agent always move through the shortest path to reach to the destination. There is a possibility of having a number of path in between the source and destination as defined in the Path Map(PM) in offline mode. If the prefferd shortest path is blocked by means of some obstraction, there must be another way to reach to the destination which have the weight of minimum among the other possible paths. The algorithm shown in this paper for shortest path, based on Kruskal's Algorithm, shows the way to find the alternative shortest path and the moving direction of the agent. In this paper, the junction-to-junction connectivity is proposed where the path search is replaced by the node search which minimises the computaional time and hence increases the effectiveness and efficiency in agent moving towards the destination from source and also in its reverse direction.

AntBeePath is a hybrid bio-inspired algorithm based on the behavior of ants and honeybees aimed at the resolution of the problem of finding the shortest paths for a given network topology. The algorithm, in brief, combines the pheromone release mechanism of existing Ant Colony Optimization (ACO) algorithms with a new bio-inspired mechanism based on the recruitment strategy of bees. Three versions of the algorithm were developed incrementally. Proof-of-concept results indicate that the AntBeePath Decay Hybrid Chain version is more efficient than the other developed versions and, beyond that, presented an improved performance in relation to an equivalent ACO algorithm. The results suggest that a hybrid algorithm, combining the ant's pheromone release with the new bio-inspired mechanism of bee recruitment along with a stagnation control mechanism can result in a new bio-inspired algorithm for path determination with improved characteristics.

2011

This paper presents the results of a research that aims to develop an algorithm to solve robot path planning (RPP) problems in static environments. The problem is to find a global optimal path that satisfies the optimization criteria of shortest path with minimum computation time. A description of a variation of Ant Colony System (ACS) algorithm utilized for Robot Path Planning (RPP) purposes is presented. A representation of heuristic and visibility equation of state transition rules is proposed to sustain the function of Ant Colony System (ACS) for solving RPP problem of finding the optimal path. This algorithm was applied within a global static map that consists of feasible free space nodes. The performance of the algorithm in terms of computation time and number of iteration required to obtain an optimal path were evaluated by using a simulation approach. Subsequently, its performance was compared to the performance of Genetic Algorithm (GA) a well known and established RPP algorithm. The results obtained indicate that the developed algorithm performed much better than the GA. In addition, an overview of robot path planning (PP) algorithms in global static environment is also offered.

2021

It is an essential issue for mobile robots to reach the target points with optimum cost which can be minimum duration or minimum fuel, depending on the problem. In this paper, it was aimed to develop a software for the optimal path planning of mobile robots in user-defined twodimensional environments with static obstacles and to analyze the performance of some optimization algorithms for this problem using this software. The developed software is designed to create obstacles of different shapes and sizes in the work area and to find the shortest path for the robot using the selected optimization algorithm. Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA) were implemented in the software. These algorithms have been tested for optimum path planning in four models with different problem sizes and different difficulty levels. When the results are evaluated, it is observed that the ABC algorithm gives better results than other algorithms in terms of the shortest distance. With this study, the use of optimization algorithms in real-time path planning of land mobile robots or unmanned aerial vehicles can be simulated.