This paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives.

本文与一组合作的飞行器一起研究未知三维环境的静态覆盖和自主探索问题。尽管这些任务通常在文献中被单独考虑，但我们提出了一个通用框架，其中的两个问题都通过单机器人优化功能的定义表述为在线获取信息的最大化，这在两种情况下仅略有不同。覆盖范围和探索的静态和动态性质。提出了一种基于这些函数的随机近似及其连续优化的通用无导数方法，从而实现了快速，分散的解决方案。这种方法的局限性限制了该解决方案具有局部最优性的保证，并且针对这两个问题提出了特定的附加层以提高最终性能。具体来说，针对覆盖问题添加了基于Voronoi的初始化步骤，并针对勘探案例提出了与基于边界的方法相结合的方法。最终生成的算法将在仿真中进行测试，并与可能的替代方法进行比较。