Implementation of Fuzzy-Based Robotic Path Planning

Davis, Divya; Supriya, P.

doi:10.1007/978-81-322-2523-2_36

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…On the other hand, Mamdani FIS contributes to the generation of better paths with respect to the objective terms; however, it needs more computation time. To this end, for a balanced solution among computation time and solution quality, the Takagi-Sugeno-Kang can be used [45,46]. Therefore, depending on the application and the needs of user, the more-suitable algorithm can be employed.…”

Section: Discussionmentioning

confidence: 99%

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Ntakolia

Lyridis

2021

JMSE

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Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.

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Section: Discussionmentioning

confidence: 99%

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Ntakolia

Lyridis

2021

JMSE

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“…GA and its modified versions are frequently implemented to find the shortest path for mobile robot path planning in different environments [17], while path planning using neural networks was developed in [18]. Integrating a path planning algorithm with the motion controllers of mobile robots was achieved in [19][20][21][22], where several different motion control strategies were employed, including fuzzy logic controls, adaptive neuro-fuzzy inference systems, and model predictive controls. The Wind Driven Optimization (WDO) and Invasive Weed Optimization (IWO) algorithms were used to tune the parameters of the fuzzy logic controller and adaptive neuro-fuzzy inference systems in [20], [21], respectively, while ACO and PSO were used in the tuning of the fuzzy logic controller presented by [23].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

166

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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed PSO-MFB algorithm is highly competitive in terms of path optimality.

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“…Depending on the environment where the robot is situated, RPP may be classified into two categories: static (environment with fixed obstacles) and International Journal of Intelligent Engineering and Systems, Vol. 15 dynamic (the environment has moving obstacles). Each of these two categories could be divided further into subgroups, global path planning (GPP), where the entire information of fixed and moving obstacles can be known ahead of time; Thus, the GPP can be prepared before the robot begins to move (offline), and there is a local path planning (LPP).…”

Section: Introductionmentioning

confidence: 99%

Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm

2022

IJIES

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This paper discusses the best path planning algorithm for an autonomous mobile robot in an unknown environment with irregular static and dynamic obstacles and a static and dynamic target based on the improved COOT (ICOOT) optimization algorithm. The ICOOT overcomes the drawbacks of unstable searches in the conventional COOT. The path planning problem is solved by finding the collision-free path between multi-objective shortest path and smoothness. The ICOOT tries to imitate the real world by adding the mobile robot's actual size and the kinematic model with specifications for mobile robots. In order to evaluate the proposed algorithm, thirteen benchmark test functions are used to make a comparison with 30, 100, and 500 dimensions. To test the efficiency of the proposed technique, results are compared with five swarm optimization algorithms. The standard deviation results show that the proposed algorithm gets the best results in 84% of the thirteen test functions for 30 dimensions and 92% for 100 and 500 dimensions. Also, in four complex maps (10×10) m, the mean results show that this method is very useful for robot paths from the start to the target, and the mean distance for ten runs is 13.3797 m for map 1, 13.5164 m for map 2, and 11.9312 m for map 3, and 16.3937 m for map 4. It showed how well it could move quickly and easily around both fixed and moving obstacles.

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Implementation of Fuzzy-Based Robotic Path Planning

Cited by 12 publications

References 3 publications

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm

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