09/08/2022 2:00am
A digital twin for #autonomous #uavs - using the tool we developed, we can simulate a UAV #swarm and even control… [LINK]
11/05/2022 12:24am
Proud to showcase our new #digitaltwin application that enables us to put simulated and real-life #autonomous[LINK]
17/03/2022 4:11am
RT @sabinehauert: Nice to present results of the @evo_nano project along with the whole team. Learn more about the EVONANO platform for the…
21/01/2022 6:27am
"Automated" (#autonomous ?) #drones soon to be at work at the Port of Hamburg. Sounds exciting! [LINK]
04/01/2022 2:04am
An interesting article on state-of-the art in #drone #sensor solutions [LINK]

[Novelty Detection with Robots Using the Grow-When-Required Neural Network]

Simulation models C++ Bash script Date: Sep 2018

DOWNLOAD CODEGET THE DATA SET Screenshot of the simulation environment
Technologies used: C++ 11, QT 5, CMake, ARGoS simulator, Bash scripts, Git

The Grow-When-Required Neural Network (GWRNN) implementation for novelty detection in simulated robot experiments using the ARGoS robot simulator.

The GWRNN [1,2] is capable of learning an input space representation in a self-organised, unsupervised fashion and detect when a novel input is presented. The network runs inside a robot that travels through the environment, progressively learns its features and recognises when a novel object is placed into the environment.

This code repository includes:
  • A library for the robot controller that allows the robot to move through an environment and perceive coloured lights
  • A class that implements the neural network and that is instantiated inside the robot program
  • Libraries that control various environments
  • Bash scripts that automatically setup and run large sets of experiments

The simulation code varies the network, robot and environmental parameters and enables precise control of the variability of the environment and of the noise in the robot sensors. Data from the experiments is stored in text files with tab-separated values, organised in folders specified for each experiment. These text files can be analysed using my custom data analysis code.


[1] Marsland, S., Shapiro, J., & Nehmzow, U. (2002). A self-organising network that grows when required. Neural Networks, 15(8–9), 1041–1058.

[2] Neto, H. V., & Nehmzow, U. (2007). Real-time automated visual inspection using mobile robots. Journal of Intelligent and Robotic Systems, 49(3), 293–307.

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