Genetic algorithms for autonomous robot navigation

Manikas, Theodore W.; Ashenayi, K.; Wainwright, Roger L.

doi:10.1109/mim.2007.4428579

Cited by 102 publications

(38 citation statements)

References 9 publications

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“…Based on the (10), modified signal for the head joint y out,head in the headnavigated locomotion is calculated from the output of the first CPG (y out,1 ) and the second CPG (y out,2 ). The final computational model is given in (12). Similarly, amplitude coefficient A head is calculated from the crossing point of y out,1 , y out,virtual , and…”

Section: B Head-navigated Locomotionmentioning

confidence: 99%

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Neurally Controlled Steering for Collision-Free Behavior of a Snake Robot

2013

IEEE Trans. Contr. Syst. Technol.

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Abstract-Biologically inspired snake robots have been widely studied for their various motion patterns. Most research has focused on the design of a controller for a given motion pattern. However, relatively limited work appears to have been done on the design of a controller for self-adaptive locomotion. In this brief, we add sensory inputs to a control system in order to investigate collision avoidance in a snake robot using a neural controller based on central pattern generator. From an analysis of the steering mechanism during serpentine locomotion, we derive a mathematical model of the joint configuration and the steering angle. In a neural oscillator network, steering control can be achieved via the proposed amplitude modulation method by modulating the neural oscillation parameters. A headnavigated motion pattern is employed to allow the range sensors to accurately detect obstacles for collision avoidance. Through the head-navigated locomotion, the head of the snake robot can be controlled to keep the orientation the same as the motion direction. The proposed control method is experimentally verified by application to the SR-I snake robot.Index Terms-Collision-free behavior, neural oscillator, snake robot, steering.

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Section: B Head-navigated Locomotionmentioning

confidence: 99%

“…Matsuo and Ishii [11] also applied the AMM to change the direction of a neurally controlled snake robot. Ye [12] summarized the methods for the turn motion of the snake robot. However, only qualitative results for the implementation of turn motion are available in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Neurally Controlled Steering for Collision-Free Behavior of a Snake Robot

2013

IEEE Trans. Contr. Syst. Technol.

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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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“…collision-free route). As shown in [20], the path planning problem of a mobile robot is a NP-hard optimization one that can only be solved by heuristic algorithms such as evolutionary computation techniques. Among various algorithms capable of handling NP-hard problems, the genetic algorithm (GA) has proven to be the simple yet effective one that has been frequently used in industry especially in mobile robotics [20].…”

Section: Introductionmentioning

confidence: 99%

A new genetic algorithm approach to smooth path planning for mobile robots

2016

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In this paper, the smooth path planning problem is considered for a mobile robot based on the genetic algorithm and the Bezier curve. The workspace of a mobile robot is described by a new grid-based representation (2 n × 2 n grids) that facilitates the operations of the adopted genetic algorithm. The chromosome of the genetic algorithm is composed of a sequence of binary numbered grids (i.e., control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. A numerical experiment is given to demonstrate the effectiveness of the proposed smooth path planning approach for a mobile robot.

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Genetic algorithms for autonomous robot navigation

Cited by 102 publications

References 9 publications

Neurally Controlled Steering for Collision-Free Behavior of a Snake Robot

Neurally Controlled Steering for Collision-Free Behavior of a Snake Robot

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

A new genetic algorithm approach to smooth path planning for mobile robots

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