Shortest Path Planning Algorithm – A Particle Swarm Optimization (PSO) Approach (original) (raw)
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Autonomous Robot Path Planning Using Particle Swarm Optimization in Static and Obstacle Environment
This paper presents a simple and effective approach for mobile robot that detects and avoids densely populated and randomly distributed same size circular shaped static obstacles. The PSO (Particle Swarm Optimization) algorithm is implemented in the proposed technique to determine the optimum route of a robot from source to destination point until any obstacle is detected on its path. Once any obstacle is detected over the optimized path, the obstacle avoidance is done by moving robot towards the nearest safe point around the obstacle’s boundary which is pre-defined and calculated. Furthermore, proposed technique calculates the next point around the obstacle’s boundary which is nearest to the predefined target. For finding the effectiveness, PSO and GA (Genetic Algorithm) is applied and simulated on three different cases. Each case has variation in the location of starting point and goal. Finally, simulation results of all three cases are compared in-terms of Number of Iterations, Path Length and Execution Time. In result, the PSO performs better than GA in all three cases and provides a collision free smooth path in simulated environment.
Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization
ArXiv, 2020
Motion planning is a key element of robotics since it empowers a robot to navigate autonomously. Particle Swarm Optimization is a simple, yet a very powerful optimization technique which has been effectively used in many complex multi-dimensional optimization problems. This paper proposes a path planning algorithm based on particle swarm optimization for computing a shortest collision-free path for a mobile robot in environments populated with static convex obstacles. The proposed algorithm finds the optimal path by performing random sampling on grid lines generated between the robot start and goal positions. Functionality of the proposed algorithm is illustrated via simulation results for different scenarios.
Now a day, there are even demands for application of robots in homes and hospitals. The goal of this research is to plan a trajectory and minimizing the path lengths with collisions avoidance for a mobile robot in dynamic environment. In this paper, an intelligent approach for navigation of a mobile robot in dynamic environment with multiple targets is proposed. Particle Swarm Optimization (PSO) method is used for finding proper solutions of optimization problems. PSO has been demonstrated to be a useful technique in robot path planning in dynamic environment with mobile obstacles and multiple goals, as a feasible approach for self organized control of robot to avoid obstacle throughout the trajectory. The authors here has been used a grid based search approach for robot. The positions of the obstacles will be changed randomly. Finally, simulation results confirm the effectiveness of our algorithm.
Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization
Pomiary Automatyka Robotyka, 2017
This paper presents a constrained Particle Swarm Optimization (PSO) algorithm for mobile robot path planning with obstacle avoidance. The optimization problem is analyzed in static and dynamic environments. A smooth path based on cubic splines is generated by the interpolation of optimization solution; the fitness function takes into consideration the path length and obstaclegenerated repulsive zones. World data transformation is introduced to reduce the optimization algorithm computational complexity. Different scenarios are used to test the algorithm in simulation and real-world experiments. In the latter case, a virtual robot following concept is exploited as part of the control strategy. The path generated by the algorithm is presented in results along with its execution by the mobile robot.
A Robust Path Planning For Mobile Robot Using Smart Particle Swarm Optimization
Procedia Computer Science, 2018
In this paper, a new approach is presented for getting a solution of the mobile robot path planning problem based on Adaptive Particle Swarm Optimization (APSO). The proposed APSO algorithm is smarter than conventional PSO and widely used for solving the real time problems. In this work an objective function is framed considering the distance between robot to goal and obstacle respectively. The objective function is optimized with of APSO for solving the path planning process of robot. The different simulated experiments are performed to test the ability of the proposed algorithm. The performance of the robot path planning using APSO is compared to the performance of the conventional PSO in terms path length and time in static environments. It is focused that using new approach the robot can successfully avoid obstacle and reach the target with shorter time than conventional PSO.
2011
Particle Swarm Optimization (PSO) has been demonstrated to be a useful technique in robot path planning in dynamic environment with mobile obstacles and goal. One or many robots are able to locate a specification target with high efficiency when 44 M. Yarmohamadi, H. Haj Seyyed Javadi and H. Erfani driven by an optimization PSO algorithm. The goal of the optimization is minimize the resultant path lengths. To avoid obstacles during movement trajectory, self organized trajectory planning is required. This study propose to use particle swarm optimization, which is motivate from the simulation of social behavior of fishes and birds, as a feasible approach for self organized control of robot to avoid obstacle throughout the trajectory.
Smooth path planning of a mobile robot using stochastic particle swarm optimization
… and Automation, Proceedings of the 2006 IEEE …, 2006
This paper proposes a new approach using improved particle swarm optimization (PSO) to optimize the path of a mobile robot through an environment containing static obstacles. Relative to many optimization methods that produce nonsmooth paths, the PSO method developed in this paper can generate smooth paths, which are more preferable for designing continuous control technologies to realize path following using mobile robots. To reduce computational cost of optimization, the stochastic PSO (S-PSO) with high exploration ability is developed, so that a swarm with small size can accomplish path planning. Simulation results validate the proposed algorithm in a mobile robot path planning.
Path Planning in Swarm Robots using Particle Swarm Optimisation on Potential Fields
2012
This article presents a novelimplementation of Particle Swarm Optimisation(PSO)forfinding the most optimal solution to path planning problem for a swarm of robots. The swarm canvasses through the configuration space having static obstaclesby applying PSO on potential fields generated by the target. The best possible path by the momentary leaders of the group is retraced toget the solution. The designed algorithm was simulated on a specially developed simulator adhering to real time constraints and conditions faced by the mobile robots. The solutions for various configuration spaces are presented to verify the effectiveness of the algorithm.
International Journal of Computers Communications & Control, 2015
To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membrane-inspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO.
A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots
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.