Hybrid Genetic-fuzzy approach to Autonomous Mobile Robot

Senthilkumar, K.S.; Bharadwaj, Kamal Kant

doi:10.1109/tepra.2009.5339649

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…An extensive use of expert knowledge is made in all of these approaches. In [12] 360 laser sensor beams are used as input data, and are heuristically combined into 8 sectors as inputs to the learning algorithm. On the other hand, in [9,13,14,15,16,18,19,21] the input variables of the learning algorithm are defined by an expert.…”

Section: Related Workmentioning

confidence: 99%

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Rodríguez-Fdez¹,

Mucientes²,

Bugarín³

2015

Applied Soft Computing

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The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.

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Section: Related Workmentioning

confidence: 99%

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Rodríguez-Fdez¹,

Mucientes²,

Bugarín³

2015

Applied Soft Computing

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“…Many researchers have proposed various techniques for autonomous mobile robot navigation and obstacle avoidance to improve the robot performance. These approaches used the artificial intelligent algorithms such as fuzzy logic, neural networks and genetic algorithms [5][6][7][8][9][10][11][12][13][14][15]. During the past several years hybrid genetic-fuzzy approach has considered as one of the most powerful technique for research of intelligent system design application [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches used the artificial intelligent algorithms such as fuzzy logic, neural networks and genetic algorithms [5][6][7][8][9][10][11][12][13][14][15]. During the past several years hybrid genetic-fuzzy approach has considered as one of the most powerful technique for research of intelligent system design application [12][13][14][15]. The Proposed controller in [12] was designed using fuzzy logics theory and then, a genetic algorithm was applied to optimize the scaling factors of the fuzzy logic controller for better accuracy and smoothness of the robot trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…The Proposed controller in [12] was designed using fuzzy logics theory and then, a genetic algorithm was applied to optimize the scaling factors of the fuzzy logic controller for better accuracy and smoothness of the robot trajectory. In [13], they provided a hybrid genetic-fuzzy approach by which an improved set of rules governing the actions and behavior of a simple navigating and obstacle avoiding autonomous mobile robot.…”

Section: Introductionmentioning

confidence: 99%

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Multi Objective Optimization of Trajectory Planning of Non-holonomic Mobile Robot in Dynamic Environment Using Enhanced GA by Fuzzy Motion Control and A*

Oleiwi

Al-Jarrah

Roth

et al. 2014

Communications in Computer and Information Science

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a new hybrid approach based on Enhanced Genetic Algorithm by modified the search A* algorithm and fuzzy logic system is proposed to enhance the searching ability greatly of robot movement towards optimal solution state in static and dynamic environment. In this work, a global optimal path with avoiding obstacles is generated initially. Then, global optimal trajectory is fed to fuzzy motion controller to be regenerated into time based trajectory. When unknown obstacles come in the trajectory, fuzzy control will decrease the robot speed. The objective function for the proposed approach is for minimizing travelling distance, travelling time, smoothness and security, avoiding the static and dynamic obstacles in the robot workspace. The simulation results show that the proposed approach is able to achieve multi objective optimization in dynamic environment efficiently.

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“…used to collect, plan and execute their route as well as perform specific tasks to achieve a specific mission. An autonomous mobile robot [7] requires efficient path planning approach. Path planning is one of the important problems in the field of robotics as it tries to find out the optimal path from source position to target position.…”

Section: Introductionmentioning

confidence: 99%