Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

Area of Science:

• Robotics
• Bio-inspired engineering
• Collective behavior

Background:

• Studying fish behavior in natural environments is challenging.
• Existing robotic flocking algorithms face limitations in underwater communication and realistic simulation.

Purpose of the Study:

• To introduce a novel flocking algorithm, the Fish-Inspired Robotic Algorithm (FIRA), for robotic fish.
• To enhance the study of fish behavior through bio-inspired robotics and indirect communication methods.

Main Methods:

• FIRA integrates standard flocking behaviors (attraction, alignment, repulsion) with predator avoidance, foraging, and obstacle avoidance.
• The algorithm incorporates predictive elements for sensor delays and memory, utilizing a stigmergy-inspired, high-latency communication method.
• Simulations were conducted using a digital twin of a bio-inspired robotic fish in a realistic environment.

Main Results:

• FIRA demonstrated superior performance in collision avoidance and exploration compared to other flocking algorithms.
• The algorithm enables realistic emergent behaviors and effective multi-agent swarm coordination with tetherless communication.
• FIRA proved to be a computationally light control algorithm suitable for low-cost agents.

Conclusions:

• FIRA is a viable and effective algorithm for mimicking fish behavior in robotics.
• The developed algorithm facilitates the creation of inexpensive, multi-agent robotic swarms for underwater research.
• FIRA paves the way for future research involving the assimilation of robots into biological fish schools.