Disciplines
Robotics
Abstract (300 words maximum)
Mosaics, as an artistic expression, involves the meticulous arrangement of diverse tiles to form a unified composition. Drawing inspiration from this concept, the field of swarm robotics seeks to emulate nature’s collective behaviors observed in ant colonies, fish schools, and bird flocks, employing multiple agents to accomplish tasks efficiently. Our research explores the concept of mosaic swarm robotics, where numerous nodes with specialized functions are deployed across various domains, including applications for outdoor data capture and environment mapping. We utilized custom mobile robots operated by Raspberry Pi microcontrollers. By establishing an elaborate web of client-to-client communications to enable true localized swarm interactions needed to procure a robust and adaptable system that can be operated through clustering techniques and wireless sensor networking. This research aims to localize swarm navigation through ArUco markers to accurately track the position of a robot in a collaborative environment. The foundational algorithms developed will not only serve the immediate purpose but also pave the way for future applications, extending to drone systems to facilitate seamless collaboration across multiple domains.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Muhammad Hassan Tanveer
Included in
Mosaic Swarm Robotics: Emulating Natural Collective Behaviors for Efficient Task Execution with Custom Mobile Robots
Mosaics, as an artistic expression, involves the meticulous arrangement of diverse tiles to form a unified composition. Drawing inspiration from this concept, the field of swarm robotics seeks to emulate nature’s collective behaviors observed in ant colonies, fish schools, and bird flocks, employing multiple agents to accomplish tasks efficiently. Our research explores the concept of mosaic swarm robotics, where numerous nodes with specialized functions are deployed across various domains, including applications for outdoor data capture and environment mapping. We utilized custom mobile robots operated by Raspberry Pi microcontrollers. By establishing an elaborate web of client-to-client communications to enable true localized swarm interactions needed to procure a robust and adaptable system that can be operated through clustering techniques and wireless sensor networking. This research aims to localize swarm navigation through ArUco markers to accurately track the position of a robot in a collaborative environment. The foundational algorithms developed will not only serve the immediate purpose but also pave the way for future applications, extending to drone systems to facilitate seamless collaboration across multiple domains.