Validating the Boid Algorithm: A Comparative Study of Simulation and Physical Swarm Performance
Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Matthew Scheinblum-Brewer, Graduate Student
Lead Presenter's Name
Matthew Scheinblum-Brewer
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Bryan Watson
Abstract
Swarm robotics enables autonomous systems to function without centralized control, with applications ranging from defense operations to space-based networks like satellite constellations. While sophisticated variations of flocking algorithms have been implemented in real-world scenarios, there is limited physical validation of Craig Reynolds’ boids algorithm, which consists of three simple steering behaviors: separation, alignment, and cohesion. Without this validation, it remains unclear how directly the foundational principles of flocking translate from simulation to real-world robotic systems. This study aims to investigate whether the most basic form of the boid algorithm, when implemented on a physical robotic swarm, behaves consistently with its simulated counterpart. By analyzing discrepancies in real-world implementation, this research seeks to establish a knowledge base on how fundamental flocking behaviors are affected by physical constraints, providing insights that will inform future iterations of more advanced models. The Bio-Inspired Swarm Test Arena for Resilient Systems (STARS), a low-cost robotic swarm developed as a platform for testing biologically inspired algorithms, will be programmed with an iteration of the boid algorithm. The swarm will consist of 25 small wheeled robots, with their behavior directly compared to an identical simulation. Key parameters, which are yet to be determined, will be measured using onboard sensors and motion tracking provided by overhead cameras. Any inconsistencies between the two environments will be analyzed to assess the influence of real-world constraints. The results will provide critical insights into the baseline behavior of decentralized flocking systems.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Ignite Grant
Validating the Boid Algorithm: A Comparative Study of Simulation and Physical Swarm Performance
Swarm robotics enables autonomous systems to function without centralized control, with applications ranging from defense operations to space-based networks like satellite constellations. While sophisticated variations of flocking algorithms have been implemented in real-world scenarios, there is limited physical validation of Craig Reynolds’ boids algorithm, which consists of three simple steering behaviors: separation, alignment, and cohesion. Without this validation, it remains unclear how directly the foundational principles of flocking translate from simulation to real-world robotic systems. This study aims to investigate whether the most basic form of the boid algorithm, when implemented on a physical robotic swarm, behaves consistently with its simulated counterpart. By analyzing discrepancies in real-world implementation, this research seeks to establish a knowledge base on how fundamental flocking behaviors are affected by physical constraints, providing insights that will inform future iterations of more advanced models. The Bio-Inspired Swarm Test Arena for Resilient Systems (STARS), a low-cost robotic swarm developed as a platform for testing biologically inspired algorithms, will be programmed with an iteration of the boid algorithm. The swarm will consist of 25 small wheeled robots, with their behavior directly compared to an identical simulation. Key parameters, which are yet to be determined, will be measured using onboard sensors and motion tracking provided by overhead cameras. Any inconsistencies between the two environments will be analyzed to assess the influence of real-world constraints. The results will provide critical insights into the baseline behavior of decentralized flocking systems.