Swarm Robotics for Caging-based Collaborative Transport of Multiple Objects

Resumo

This work presents a swarm robotics approach for caging-based transportation of multiple objects in automated environments. The motivation stems from the challenge of enabling a swarm of autonomous robots to efficiently locate, coordinate, and transport multiple objects simultaneously, while avoiding collisions and maintaining group cohesion. The proposed model simulates a group of mobile robots that explore a predefined arena using a foraging strategy to search for objects distributed across the environment. Upon locating an object, the detecting robot assumes the role of recruiter, estimating the required number of collaborators based on the object’s mass and dynamically selecting the nearest available robots to form a caging formation. Once the formation is established, the swarm initiates the transport operation, maintaining alignment and coordination throughout the trajectory. To support multiple simultaneous transport tasks, the system incorporates a path conflict detection and resolution mechanism. This mechanism prevents collisions at intersection points by prioritizing the group nearest to the crossing, while others temporarily yield until safe passage is confirmed. Key challenges addressed include real-time inter-robot synchronization, precise agent positioning around objects, and the management of intergroup priorities to prevent collisions without compromising efficiency. These challenges are addressed through a combination of geometric path planning, signal-based communication, and modular control logic, resulting in a robust and scalable coordination framework. The conflict resolution mechanism effectively prevents collisions between robot groups, preserving the integrity of transport operations even in densely populated environments. The methodology is implemented in the CoppeliaSim simulation environment using Khepera-III mobile robots. Simulation results, in terms of time requirements regarding object search, robots’ recruitment and object transport as well as transport path error, demonstrate that multiple swarms can successfully and efficiently transport multiple objects to their designated targets. The system also exhibits strong adaptability, maintaining coordinated behavior under varying robot and object densities.