Accelerating Resource-Constrained Swarm Robotics With Cone-Based Loop Closure and 6G Communication

Abstract

Loop closure detection, a critical component of Simultaneous Localization and Mapping (SLAM) systems, can be computationally intensive, particularly in swarm robotics where coordination among multiple agents is essential. Traditional SLAM methods often involve comparing each frame with all previous frames, leading to performance bottlenecks, especially on battery-operated or resource-constrained devices. This leaves little room for other critical tasks, such as continuous coordination and information sharing among swarm robots, which require swift execution to perform effectively. This paper introduces a novel cone-based approach to streamline loop closure detection, significantly reducing the computational burden and improving system efficiency. By limiting frame comparisons to a predefined region, our method accelerates SLAM algorithms, enabling more efficient coordination and exploration among swarm robots. The approach is particularly advantageous for resource-constrained devices and battery-powered platforms operating in dynamic environments. The need for millisecond-level response times in swarm robotic tasks require the integration of 6G networks for seamless communication and coordination. Experimental results demonstrate the effectiveness of the cone-based method in enhancing loop closure accuracy while minimizing computational overhead. This makes it a valuable tool for advancing swarm robotic applications, particularly in 6G-enabled environments where real-time coordination and efficiency are paramount.