Abstract The consensus of multi-agent dynamic systems is a metaphor for many different tasks involving group agreement. However, ensuring consensus in real-world scenarios, with non-convex obstacles and kinodynamic motion constraints, proves to be a hard task, since it is quite difficult to model such a problem analytically. Therefore, this paper studies the problem of state agreement for Multi-Robot Systems (MRS) in unknown cluttered complex environments. Here, we propose and analyze a distributed consensus algorithm combined with a Rapidly-exploring Random Tree-based planner, which allows linear and nonlinear systems to reach a common target on their states inside bi- or three-dimensional spaces filled with static obstacles. We demonstrate that, with enough time, our planning strategy ensures probabilistic completeness convergence independently of the topological communication network employed, since some connectivity constraints are observed. Simulated results with linear and nonlinear models are provided, showing the effectiveness of our proposed method in comparison with the state-of-the-art literature for the specific case of position consensus (rendezvous) missions.

中文翻译：

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基于随机规划的未知杂乱环境中非线性代理的共识

摘要 多智能体动态系统的共识是对涉及群体协议的许多不同任务的隐喻。然而，在具有非凸形障碍物和运动动力学约束的现实世界场景中确保共识被证明是一项艰巨的任务，因为对这样的问题进行分析建模非常困难。因此，本文研究了未知杂乱复杂环境中多机器人系统（MRS）的状态一致性问题。在这里，我们提出并分析了一种分布式共识算法，结合了基于快速探索随机树的规划器，该算法允许线性和非线性系统在充满静态障碍的二维或三维空间内达到其状态的共同目标。我们证明，只要有足够的时间，我们的规划策略确保独立于所采用的拓扑通信网络的概率完整性收敛，因为观察到一些连接约束。提供了具有线性和非线性模型的模拟结果，显示了我们提出的方法与位置共识（会合）​​任务特定情况下的最新文献相比的有效性。