Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Rasika Ravindra Kale, Graduate Student James Hand, Ph.D. candidate Jonah Kohlmeyer, Graduate student Dawson Jones, Senior Nathan Geroge, Junior
Lead Presenter's Name
Rasika Ravindra Kale
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Bryan Watson
Abstract
Swarm robotics presents a robust platform for investigating distributed control, adaptive behavior, and autonomous coordination within a system of systems framework. The Roving Swarm comprises 24 low-computation robots, emulating eusocial insect behavior through vision-sensing and decentralized decision-making. A key subsystem, the motion-sensing and interaction module, integrates real-time object detection, swarm coordination, and environmental responsiveness, employing Pixy2 vision sensors and Arduino 33 IoT controllers. The robots use DC motors for actuation, enabling structured rotational and translational motion, and demonstrate dynamic adaptability to environmental cues such as light intensity and peer movements. Designed for modularity and scalability, the system lifecycle of the swarm supports enhancements such as additional sensory inputs, improved localization techniques, and advanced motion planning algorithms. A controlled testbed, incorporating an overhead tracking system, boundary constraints, and adjustable lighting, ensures repeatable, safe, and systematic evaluation of swarm behaviors. This lifecycle approach facilitates the analysis of emergent properties, refinement of coordination strategies, and bolstering of resilience across decentralized multi-agent systems. Beyond its research applications, the platform doubles as an experiential educational tool in robotics, artificial intelligence, and cyber-physical systems. A curriculum rooted in the swarm system immerses students in real-world problem-solving, embedded systems programming, mechatronics, and bio-inspired algorithm development. Planned lifecycle advancements include incorporating additional sensory modalities, leveraging machine learning for enhanced object tracking, and fostering cooperative behaviors in dynamic environments. Through a systems-of-systems approach that blends robotics, control theory, and bio-inspired computing, the Roving Swarm exemplifies technical innovation and experiential learning. It bridges theoretical concepts with practical deployment, fostering both academic and applied advancements.
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?
No
STARS: System lifecycle perspective of an autonomous robotic swarm test arena
Swarm robotics presents a robust platform for investigating distributed control, adaptive behavior, and autonomous coordination within a system of systems framework. The Roving Swarm comprises 24 low-computation robots, emulating eusocial insect behavior through vision-sensing and decentralized decision-making. A key subsystem, the motion-sensing and interaction module, integrates real-time object detection, swarm coordination, and environmental responsiveness, employing Pixy2 vision sensors and Arduino 33 IoT controllers. The robots use DC motors for actuation, enabling structured rotational and translational motion, and demonstrate dynamic adaptability to environmental cues such as light intensity and peer movements. Designed for modularity and scalability, the system lifecycle of the swarm supports enhancements such as additional sensory inputs, improved localization techniques, and advanced motion planning algorithms. A controlled testbed, incorporating an overhead tracking system, boundary constraints, and adjustable lighting, ensures repeatable, safe, and systematic evaluation of swarm behaviors. This lifecycle approach facilitates the analysis of emergent properties, refinement of coordination strategies, and bolstering of resilience across decentralized multi-agent systems. Beyond its research applications, the platform doubles as an experiential educational tool in robotics, artificial intelligence, and cyber-physical systems. A curriculum rooted in the swarm system immerses students in real-world problem-solving, embedded systems programming, mechatronics, and bio-inspired algorithm development. Planned lifecycle advancements include incorporating additional sensory modalities, leveraging machine learning for enhanced object tracking, and fostering cooperative behaviors in dynamic environments. Through a systems-of-systems approach that blends robotics, control theory, and bio-inspired computing, the Roving Swarm exemplifies technical innovation and experiential learning. It bridges theoretical concepts with practical deployment, fostering both academic and applied advancements.