Neural Networks in Mobile Robot Motion

By D. Janglova. 2004

209 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

A review: On path planning strategies for navigation of mobile robot (2019, background): “Janglova [105] presented the application of a NN for a wheeled mobile robot navigation in a partially unknown environment.”
Sampling-Based Robot Motion Planning: A Review (2014, methodology): “Methods such as Fuzzy Logic Control [33]–[35], Neural Networks [36], Genetic Algorithms [37], [38], Ant ColonyOptimization [39] and SimulatedAnnealing [20] have all been applied in robot path planning.”
Heuristic approaches in robot path planning: A survey (2016, methodology): “Janglova [44] constructed collision-free path based on two neural networks: principal component analysis (PCA) and a multilayer perceptron (MLP), in Fig.”