To steer a group of agents to maneuver with the desired collective forms, this paper studies the affine formation maneuver control of high-order multi-agent systems using a two-layered leader-follower strategy, in which agents are divided into three types: first leader, second leader group, and followers. The first leader will decide the whole formation's maneuver parameters, and the second leader group will keep the desired relative positions with respect to the first leader. The followers aim at moving to the desired positions affinely localized by the leaders. The main feature of this two-layered leader-follower strategy is that the information interaction from the first leader to the second leader group requires wireless communication, and only the measurements of local relative position and its finite-order time-derivatives are needed for the followers. To achieve the control objective under the given constraints, the distributed control algorithms are designed for the second leader group and followers, in which the backstepping, distributed estimation, $PD^m$ , and adaptive gain design techniques are employed. Finally, a simulation example with obstacle avoidance is provided to validate the effectiveness of the proposed control algorithms.

Due to the nonsymmetric property of the associated Laplacian matrix and the existence of uncertainties in the agent dynamics, the leaderless consensus problem of uncertain multiagent systems under general directed graphs is challenging. Motivated by the classical model reference adaptive control, in this article, we propose a simple yet efficient scheme, called the model reference adaptive consensus, by arranging each agent a reference output to track, where the output is generated by a linear reference model with the relative state measurements as input. We consider two typical agent dynamics, namely, the general linear dynamics with matching uncertainties and the second-order dynamics with extended matching uncertainties. Different linear reference models are designed for the above uncertain agent dynamics. For the first one, dynamical consensus is achieved under a fixed directed graph containing a directed spanning tree. For the latter one, position consensus is achieved under switching uniformly jointly connected graphs. In the proposed algorithms, only the relative states are interacted among the agents and there is no requirement on the communication of virtual signals.

This study is concerned with the problem of cooperative formation‐containment control (CFCC) for networked Euler–Lagrange systems under a directed graph. Compared with the distributed containment control problem, CFCC problem is a new problem in the community of multi‐agent systems as the control for leaders' formation is further considered. By introducing the desired relative distance between agents into the auxiliary variables, the formation control algorithms are designed firstly for multiple leaders to reach a constant configuration. Then, the distributed containment control algorithms are designed for the followers such that all followers asymptotically converge to the convex hull spanned by the leaders. In the proposed algorithms, the relative velocity information is not required for the agents' controller design. Moreover, to reduce the dependence on system global information, the control gains in the algorithms are changing adaptively using only local information. Finally, simulation results are provided to illustrate the effectiveness of the obtained theoretical results.

In this paper, we study the leaderless consensus problem for multiple Lagrangian systems in the presence of parametric uncertainties and external disturbances under directed graphs. For achieving asymptotic behavior, a robust continuous term with adaptive varying gains is added to alleviate the effects of the external disturbances with unknown bounds. In the case of a fixed directed graph, by introducing an integrate term in the auxiliary variable design, the final consensus equilibrium can be explicitly derived. We show that the agents achieve weighted average consensus, where the final equilibrium is dependent on three factors, namely, the interactive topology, the initial positions of the agents, and the control gains of the proposed control algorithm. In the case of switching directed graphs, a model reference adaptive consensus based algorithm is proposed such that the agents achieve leaderless consensus if the infinite sequence of switching graphs is uniformly jointly connected. Motivated by the fact that the relative velocity information is difficult to obtain accurately, we further propose a leaderless consensus algorithm with gain adaptation for multiple Lagrangian systems without using neighbors’ velocity information. We also propose a model reference adaptive consensus based algorithm without using neighbors’ velocity information for switching directed graphs. The proposed algorithms are distributed in the sense of using local information from its neighbors and using no comment control gains. Numerical simulations are performed to show the effectiveness of the proposed algorithms.

In this paper, we investigate the leaderless consensus problem for second-order multi-agent systems (MASs) with unknown inertias and parametric uncertainties. The graph that describes the interaction among the agents is generally directed. We consider both fixed and switching graphs. For a fixed directed topology, the associated graph needs to contain a spanning tree. For dynamically switching topologies, the uniform joint connectivity is required. We proposed consensus algorithms without relative velocity measurements, which are fully distributed in the sense that no common control gains are used and no global gain dependency conditions are required. The cases of agent dynamics with unknown nonidentical control directions and the asymptotic consensus in the presence of external disturbances are further investigated.

In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state estimation and scene reconstruction. However, most of the related works assume a simple world for semantic association. It is still a challenge to deal with the ambiguity of data association in a cluttered environment. In this article, we propose a novel approach to reduce the uncertainty of data association via multiple hypothesis Dirichlet process (MHDP). The posterior distribution of data association is inferred by Dirichlet process (DP) first. Ambiguous associations from the distribution are tackled by a hypothesis tree, and hypothesis testing-based ambiguity judgment is then proposed for each object measurement to provide a strategy for branch growing of the hypothesis tree. Moreover, the proposed data association approach is integrated with a geometric featured-based simultaneous localization and mapping (SLAM) system in a tightly coupled way. The qualitative and quantitative evaluation on simulated and real-world public data sets demonstrates the robustness and effectiveness of our approach compared to other data association methods and the state-of-the-art SLAM system.