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In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

This article considers the distributed leader–follower formation tracking control problem for a networked linear multiagent system (MAS) consisting of one nonautonomous leader and multiple homogeneous followers in a sample-data-based edge-event-triggering communication setting. A novel edge-state-estimate-based triggering function along with a triggering rule is proposed for each communication edge to regulate and reduce unnecessary data transmission. A new distributed formation tracking protocol is then developed for each follower based on only event-generator-regulated information. The formation tracking problem is reformulated as a stability analysis problem of a delayed system by defining the formation error dynamics. Lyapunov-based methods and linear matrix inequality (LMI) techniques are utilized to derive sufficient conditions for codesigning the event-generator and controller gains that ensure the asymptotic and exponential convergence of the closed-loop formation error dynamics. Numerical simulations and experimental implementations were carried out using a group of four unicycle-type mobile robots to demonstrate and validate the effectiveness of the proposed method.

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