Autonomous robot navigation system using a novel value encoded genetic algorithm

Geisler, T.; Manikas, Theodore W.

doi:10.1109/mwscas.2002.1186966

Cited by 20 publications

(18 citation statements)

References 6 publications

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“…In order to generate a trajectory from the initial to the desired location in obstacle avoidance conditions, the robot must also possess perception, reasoning, and recognition characteristics, as well as the ability to learn and approximate any function with an appropriate generalization performance. For decades, various types of navigation methods have been developed to deal with the motion planning problem of mobile robots, such as the artificial potential field method [2,3], genetic algorithm (GA) method [4][5][6], particle swarm optimization (PSO) [7,8], and so on. However, these methods suffer from some inherent drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A neural network approach to navigation of a mobile robot and obstacle avoidance in dynamic and unknown environments

Shamsfakhr¹,

Bigham²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Mobile robot navigation and obstacle avoidance in dynamic and unknown environments is one of the most challenging problems in the field of robotics. Considering that a robot must be able to interact with the surrounding environment and respond to it in real time, and given the limited sensing range, inaccurate data, and noisy sensor readings, this problem becomes even more acute. In this paper, we attempt to develop a neural network approach equipped with statistical dimension reduction techniques to perform exact and fast robot navigation, as well as obstacle avoidance in such a manner. In order to increase the speed and precision of the network learning and reduce the noise, kernel principal component analysis is applied to the training patterns of the network. The proposed method uses two feedforward neural networks based on function approximation with a backpropagation learning algorithm. Two different data sets are used for training the networks. In order to visualize the robot environment, 180• laser range sensor (SICK) readings are employed. The method is tested on real-world data and experimental results are included to verify the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A neural network approach to navigation of a mobile robot and obstacle avoidance in dynamic and unknown environments

Shamsfakhr¹,

Bigham²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…The previous genotype by a member of our research group, T. Geisler, [4,5] contained only two variables, which will be discussed later, Path-Location and Path-Direction. That encoding technique allowed only row-wise movements.…”

Section: Encoding Techniquementioning

confidence: 99%

“…As discussed earlier, the previous work by a member of our group, T. Geisler [4,5], only allowed row-wise movements. Next, Aditia Hermanu [6] modified the genotype so that the robot was able to plan either a row-wise or a column-wise movement according to the search space arrangements.…”

Section: C4) Gene Structure: Path-switchmentioning

confidence: 99%

“…The work presented here is part of a larger project to build an autonomous path-planning robot. This research is motivated by earlier work in this field of interest [4][5][6] by the same research team. This paper presents the research and simulation results of a genetic algorithm based path-planning software.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents the research and simulation results of a genetic algorithm based path-planning software. The algorithm uses an improved, modified version of previous encoding techniques [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous local path planning for a mobile robot using a genetic algorithm

Sedighi

Ashenayi

Manikas

et al.

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

Self Cite

136

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Abstract-This paper presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed pathplanning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

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A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning

et al. 2021

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Autonomous robot navigation system using a novel value encoded genetic algorithm

Cited by 20 publications

References 6 publications

A neural network approach to navigation of a mobile robot and obstacle avoidance in dynamic and unknown environments

A neural network approach to navigation of a mobile robot and obstacle avoidance in dynamic and unknown environments

Autonomous local path planning for a mobile robot using a genetic algorithm

A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning

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