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Neural Networks in Mobile Robot Motion
TLDR
The approach to solving the motion-planning problem in mobile robot control using neural networks-based technique and the method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks.Abstract:
This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the "free" space using ultrasound range finder data. The second neural network "finds" a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.read more
Citations
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Journal ArticleDOI
Sampling-Based Robot Motion Planning: A Review
Mohamed Elbanhawi,Milan Simic +1 more
TL;DR: The state of the art in motion planning is surveyed and selected planners that tackle current issues in robotics are addressed, for instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed.
Journal ArticleDOI
Heuristic approaches in robot path planning
TL;DR: This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature inspired algorithms and hybrid algorithms.
Journal ArticleDOI
A review: On path planning strategies for navigation of mobile robot
TL;DR: It has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches and are used to improve the performance of the classical approaches as a hybrid algorithm.
Journal ArticleDOI
Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review
TL;DR: The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments.
Journal ArticleDOI
Path planning in uncertain environment by using firefly algorithm
TL;DR: The proposed controller solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies, and the performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.
References
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Book
Robot Motion Planning
TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Journal ArticleDOI
Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
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Robot Motion: Planning and Control
TL;DR: In this article, the authors present nineteen papers of fundamental importance to the development of a science of robotics, grouped in five sections: Dynamics; Trajectory Planning; Compliance and Force Control; Feedback Control; and Spatial Planning.
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Models of Neural Networks
TL;DR: In this article, collective phenomena in neural networks are discussed, which is applied in subsequent chapters to the specific areas of dynamics and storage capacity of networks of formal neurons with symmetric or asymmetric couplings, learning algorithms, temporal association, structured data (software) and structural nets (hardware).
Journal ArticleDOI
Model-based learning for mobile robot navigation from the dynamical systems perspective
TL;DR: This paper discusses how a behavior-based robot can construct a "symbolic process" that accounts for its deliberative thinking processes using models of the environment and shows that the robot is capable of learning grammatical structure hidden in the geometry of the workspace from the local sensory inputs through its navigational experiences.