A Robust Path Planning For Mobile Robot Using Smart Particle Swarm Optimization (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.
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.
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...
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.
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.
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.
Mobile robot navigation using particle swarm optimization and adaptive NN
Advances in Natural Computation, 2005
This paper presents a novel design for mobile robot using particle swarm optimization (PSO) and adaptive NN control. The adaptive NN control strategy guarantees that robot with nonholonomic constraints can follow smooth trajectories. Based on this property, a PSO algorithm for path planning is proposed. The path planning generates smooth path with low computational cost to avoid obstacles, so that robot can use smooth control strategy to track the trajectory.
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...
2015
Now-a-days autonomous mobile robots have found applications in diverse fields. An Autonomous robot system must be able to behave in an intelligent manner to deal with complex and changing environment. This work proposes the performance of path planning and navigation of autonomous mobile robot using Gravitational Search Algorithm (GSA), Simulated Annealing (SA) and Particle Swarm optimization (PSO) based intelligent controllers in an unstructured environment. The approach not only finds a valid collision free path but also optimal one. The main aim of the work is to minimize the length of the path and duration of travel from a starting point to a target while moving in an unknown environment with obstacles without collision. Finally, a comparison is made between the three controllers. It found that the path length and time duration made by the robot using GSA is better than SA and PSO based controllers for the same work Key-Words: Autonomous Mobile Robot; Collision Free Path; Gravit...