Autonomous Robot Path Planning Using Particle Swarm Optimization in Static and Obstacle Environment (original) (raw)
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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.
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.
Shortest Path Planning Algorithm – A Particle Swarm Optimization (PSO) Approach
2018
Path planning for a mobile robot is a difficult task and has been widely studied in robotics. The objective of recent researches is not just to find feasible paths but to find paths that are optimal with respect to distance covered and safety of the robot. Techniques based on optimization have been proposed to solve this problem but some of them used techniques that may converge to local minimum. In this paper, we present a global path planning algorithm for a mobile robot in a known environment with static obstacles. This algorithm finds the optimal path with respect to distance covered. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. Our customized algorithm generates the coordinates of the search space and passes the result to the PSO algorithm which then uses the coordinate values to determine the optimal path from start to finish. We perform our experiments usin...
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.
Turkish Journal of Science and Technology, 2021
In the movement of autonomous mobile robots in static or dynamic environments, one of the important issues sought for a solution is to reach the target with the shortest and safest path without collision. For this purpose, there are many algorithms. The solutions brought by these algorithms differ according to the dynamics of the environment. However, as is known, the real world environment is complex. As the environment gets more complex, more environment knowledge is required for the performance of the algorithms. Complex mobile robotic systems equipped with sensors are required to obtain environmental information. This causes more energy consumption, processing load and the formation of heavy structures. In order to solve these problems, there are algorithms that perform path planning without the need for all environment information. Two of these are the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. In the literature review, it is seen that these algorithms are effective in the selection and use of sensors according to the nature of the environment. However, in this respect, it was seen that their performances in static environments with different object densities were not analysed and compared. Therefore, in this study, the performance of both algorithms was compared according to the object density in the environment. Distance, time, curvature, and processing speed analyses were performed in MATLAB / Simulink environment according to different density environment scenarios.
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.
A Comparative Study for Wheeled Mobile Robot Path Planning Based on Modified Intelligent Algorithms
THE IRAQI JOURNAL FOR MECHANICAL AND MATERIALS ENGINEERING
From the time being, there are even instances for application of mobile robots in our lifelike in home, schools, hospitals, etc. The goal of this paper is to plan a path and minimizing thepath lengths with obstacles avoidance for a mobile robot in static environment. In this work wedepict the issue of off-line wheeled mobile robot (WMR) path planning, which best route forwheeled mobile robot from a start point to a target at a plane environment represented by 2-Dwork space. A modified optimization technique to solve the problem of path planning problemusing particle swarm optimization (PSO) method is given. PSO is a swarm intelligence basedstochastic optimization technique which imitate the social behavior of fish schooling or birdflocking, was applied to locate the optimum route for mobile robot so as to reach a target.Simulation results, which executed using MATLAB 2014 programming language, confirmedthat the suggested algorithm outperforms the standard version of PSO algorithm wi...
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.