Implementation Guidelines for Ethologically Inspired Fuzzy Behaviour-Based Systems

Abstract

The adaptation of ethologically inspired behaviour models for human-machine interaction e.g. in Ethorobotics has become a challenging research topic in recent years. This paper presents a Fuzzy Behaviour Description Language (FBDL) approach for analyzing animal aggression behaviour. Fuzzy logic and fuzzy set theory approaches are used to analyze and classify the subjective impression of aggressive behaviour in a particular situation. This research aims to perform a meta analysis of aggression behaviour based on the fundamental values of animals and the possible ways of implementing animal aggressive behaviour in robots. Ultimately aiming to enhance the adaptability and effectiveness of human-robot interaction and performance in various real-world scenarios, e.g., by expressing disagreement in the direction of the human operator in case of unclear, or unsafe cooperative situations. In both industrial and everyday settings, mobile robots and robotic vehicles are becoming increasingly prevalent. Integrating aggressive behaviour into robotics is essential for boosting interactions between humans and robots, promoting safety in dynamic contexts, and getting a deeper understanding of animal behaviour. It aids robots in asserting their presence, maneuvering around barriers, and efficiently adjusting to dynamic surroundings. This guarantees more seamless operations in industrial and daily environments while also enhancing our comprehension of both robotics and ethology. We present graphical depictions of various animal behaviours, as well as trajectories, Gazebo simulations, and RViz visualizations of the animal robot, demonstrating the animal’s escape behaviour.