CoSPAM: Multi-Robot Collaboration Simultaneous Path Planning and Semantic Mapping

Abstract

In recent years, with the rapid development of mobile network communication and robotic vehicle technolo-gies, there has been a growing emphasis on the design and development of sustainable intelligent robotic vehicle systems. To address this trend, we propose CoSPAM, an innovative multi-robot collaboration for simultaneous path planning and semantic mapping in a large-scale environment. CoSPAM integrates local and global strategies to optimize planning and mapping processes. Each robot operates autonomously and executes local rapidly exploring random tree exploration within its designated spatial environment. The robots establish wireless communication to transmit local map information to the server. We employ Bayesian-based fusion and Bundle Adjustment-based optimization techniques in the server to construct a comprehensive global 3D semantic OctoMap. Based on spatial memory mechanisms and time constraints, we utilize knowledge from all robots to formulate a cohesive global path plan. The experimental results demonstrate that CoSPAM reduces exploration time by almost 56.3% compared to traditional methods. It optimally allocates each robot's exploration goals, minimizing the exploration path's length. Moreover, CoSPamdemonstrates its capability to gen-erate a high-quality 3D semantic OctoMap. CoSPAM offers a reliable, agile, and energy-efficient solution for large-scale environment planning and mapping among sustainable intelligent robotic vehicles.