We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Connected Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time. MCFS uniquely enables the orchestration of multiple robots to generate coverage paths that contour around arbitrarily shaped obstacles, a feature that is notably lacking in traditional methods. Our framework not only enhances area coverage and optimizes task performance, particularly in terms of makespan, for workspaces rich in irregular obstacles but also addresses the challenges of path continuity and curvature critical for non-holonomic robots by generating smooth paths without decomposing the workspace. MCFS solves MCPP by constructing a graph of isolines and transforming MCPP into a combinatorial optimization problem, aiming to minimize the makespan while covering all vertices. Our contributions include developing a unified CFS version for scalable and adaptable MCPP, extending it to MCPP with novel optimization techniques for cost reduction and path continuity and smoothness, and demonstrating through extensive experiments that MCFS outperforms existing MCPP methods in makespan, path curvature, coverage ratio, and overlapping ratio. Our research marks a significant step in MCPP, showcasing the fusion of computer graphics and automated planning principles to advance the capabilities of multi-robot systems in complex environments. Our code is available at https://github.com/reso1/MCFS.

翻译：我们提出多机器人连接费马螺线（MCFS）——一种新颖的多机器人覆盖路径规划（MCPP）算法框架，首次将计算机图形学领域的连接费马螺线（CFS）应用于多机器人协同。MCFS独特地实现了多机器人的协同编排，能够生成绕任意形状障碍物轮廓的覆盖路径，这一特性是传统方法所明显缺失的。我们的框架不仅增强了面积覆盖率并优化了任务性能（尤其在总完工时间方面），适用于不规则障碍物密集的工作空间，还通过在不分解工作空间的情况下生成平滑路径，解决了非完整机器人对路径连续性和曲率的严苛要求。MCFS通过构建等值线图将MCPP转化为组合优化问题，在覆盖所有顶点时最小化总完工时间。我们的贡献包括：开发用于可扩展和自适应MCPP的统一CFS版本；将其扩展至MCPP并引入新颖的优化技术以降低成本、提升路径连续性与平滑度；通过大量实验证明MCFS在总完工时间、路径曲率、覆盖率及重叠率上均优于现有MCPP方法。本研究标志着MCPP领域的重要进步，展示了计算机图形学与自动化规划原理的融合如何提升多机器人系统在复杂环境中的能力。我们的代码开源在 https://github.com/reso1/MCFS。