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：我们提出了多机器人连接费马螺旋（MCFS），这是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旋（CFS）应用于多机器人协同。MCFS独特地实现了多个机器人的协调，使其能够生成围绕任意形状障碍物轮廓的覆盖路径，这一特性在传统方法中明显缺失。我们的框架不仅增强了面积覆盖并优化了任务性能（尤其在处理富含不规则障碍物的工作空间时针对完工时间），还通过生成无需分解工作空间的平滑路径，解决了非完整机器人对路径连续性和曲率的关键挑战。MCFS通过构建等值线图并将MCPP转化为组合优化问题来解决多机器人覆盖路径规划，旨在最小化完工时间的同时覆盖所有顶点。我们的贡献包括：开发可扩展且适应性强的统一CFS版本用于MCPP；通过新颖的优化技术将其扩展至MCPP，实现成本降低及路径连续性与平滑性；并通过广泛实验证明，MCFS在完工时间、路径曲率、覆盖率和重叠率方面优于现有MCPP方法。我们的研究标志着MCPP的重要进展，展示了计算机图形学与自动化规划原理的融合，以提升多机器人系统在复杂环境中的能力。我们的代码可在 https://github.com/reso1/MCFS 获取。