A Robust Path Planning For Mobile Robot Using Smart Particle Swarm Optimization

Dewang, Harshal S.; Mohanty, Prases K.; Kundu, Shubhasri

doi:10.1016/j.procs.2018.07.036

Cited by 82 publications

(48 citation statements)

References 10 publications

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“…The interpolation-based path planning in a grid environment is presented in Reference [11]. Adaptive particle swarm optimization (APSO) used in Reference [12] was used to optimize the objective function of a mobile robot, which is the distance between robot to goal and obstacle. In Reference [13], the authors hybridized the artificial potential field (APF) algorithm with an enhanced genetic algorithm (EGA) to find the shortest and smoothest path for a multi-robot.…”

Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

131

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Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

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Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

131

View full text Add to dashboard Cite

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“…A common way to address this issue requires human intervention to propose or decide the number of path elements (points in the Cartesian plane) to be adjusted. For example, an improvement of the Particle Swarm Optimization (PSO) is proposed in [33] to optimize a mobile robot's trajectory. PSO can place the points that the robot travels in each iteration, solving an optimization problem that considers the distance to the target point and the obstacles' separation.…”

Section: Optimization In Path-planningmentioning

confidence: 99%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

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“…With this algorithm, the robot has reached the target point in a shorter time than the traditional PSO algorithm. [9]. Low et al improved the performance of the Q-learning algorithm using the Flower Pollination algorithm because of the slow convergence rate of the Qlearning algorithm in the global path planning of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots

Yildirim¹,

Akay²

2021

Sakarya University Journal of Science

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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.

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A Robust Path Planning For Mobile Robot Using Smart Particle Swarm Optimization

Cited by 82 publications

References 10 publications

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots

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