In cooperative multi-agent robotic systems, coordination is necessary in order to complete a given task. Important examples include search and rescue, operations in hazardous environments, and environmental monitoring. Coordination, in turn, requires simultaneous satisfaction of safety critical constraints, in the form of state and input constraints, and a connectivity constraint, in order to ensure that at every time instant there exists a communication path between every pair of agents in the network. In this work, we present a model predictive controller that tackles the problem of performing multi-agent coordination while simultaneously satisfying safety critical and connectivity constraints. The former is formulated in the form of state and input constraints and the latter as a constraint on the second smallest eigenvalue of the associated communication graph Laplacian matrix, also known as Fiedler eigenvalue, which enforces the connectivity of the communication network. We propose a sequential quadratic programming formulation to solve the associated optimization problem that is amenable to distributed optimization, making the proposed solution suitable for control of multi-agent robotics systems relying on local computation. Finally, the effectiveness of the algorithm is highlighted with a numerical simulation.
翻译:在多试剂机器人合作系统中,为了完成既定任务,必须进行协调。重要的例子包括搜索和救援、危险环境中的行动和环境监测。而协调则要求同时满足以状态和输入限制形式出现的安全关键限制因素,以及连接限制,以确保网络中每对代理之间每对代理都有一个通信路径。在这项工作中,我们提出了一个模型预测控制器,处理多试剂协调问题,同时满足安全关键和连通性限制。前者以状态和输入限制的形式提出,后者是相关的拉普拉西亚通讯图(又称Fiedler eigenvalue)第二小电子值的限制,该图是通信网络连通性的一个工具。我们提出一个连续的四重程序设计,以解决相关的优化问题,使拟议的解决方案适合于控制依赖本地计算的多试剂机器人系统。最后,以数字模拟的方式强调了算法的有效性。</s>