A spiking neural network for behavior learning of a mobile robot in a dynamic environment

In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.

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A biomimetic spiking neural network of the auditory midbrain for mobile robot sound localisation in reverberant environments

2009 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2009.5178672 ◽

2009 ◽

Author(s):

Jindong Liu ◽

David Perez-Gonzalez ◽

Adrian Rees ◽

Harry Erwin ◽

Stefan Wermter

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Spiking Neural Network ◽

Auditory Midbrain ◽

Sound Localisation ◽

Reverberant Environments

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Trajectory Planning for a Mobile Robot in a Dynamic Environment using an LSTM Neural Network

Applied Sciences ◽

10.3390/app112210689 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10689

Author(s):

Alejandra Molina-Leal ◽

Alfonso Gómez-Espinosa ◽

Jesús Arturo Escobedo Cabello ◽

Enrique Cuan-Urquizo ◽

Sergio R Cruz-Ramírez

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Target Location ◽

Short Term Memory ◽

Dynamic Environment ◽

Test Accuracy ◽

Safe Navigation ◽

Navigation Path ◽

Sample Data ◽

Dynamic Obstacle

Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a 15 cm radius of a trained destination position.

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