Multi-robot navigation in complex environments relies on inter-robot communication and mutual observation for situational awareness. This paper studies the multi-robot navigation problem in unknown environments with line-ofsight (LoS) connectivity constraints. While previous works are limited to known environment models to derive the LoS constraints between robots, this paper eliminates such requirements by directly formulating the LoS constraints from realtime LiDAR scans, adopting techniques in point cloud visibility analysis. Based on that, we propose a novel LoS-distance metric to quantify both the urgency and sensitivity of losing LoS between robots considering their potential movements. Moreover, to address the imbalanced urgency of losing LoS between two robots, we design a fusion function to capture the overall urgency while generating gradients that facilitate robots' collaborative behavior to maintain LoS. The team connectivity is guaranteed by encoding the LoS constraints into a potential function that preserves the positivity of the Fiedler eigenvalue of robots' underlying graph. Finally, we establish a LoS-constrained exploration framework integrating the proposed connectivity controller. We showcase its applications in multi-robot exploration in complex unknown environments, where robots can always maintain the LoS connectivity through distributed sensing and communication while collaboratively exploring unknown environments. Our implementations are available at https://github.com/bairuofei/LoS_constrained_navigation.

Angle-constrained formation control has garnered significant attention owing to the advantage of interedge angles invariant under translation, rotation, and scaling. However, most existing approaches addressing this problem are applicable only to single- or double-integrator dynamics, which are often impractical in real-world scenarios. In this article, an angle rigidity-based adaptive formation control framework is introduced for nonlinear multiagent systems subject to mismatched uncertainties. The proposed control framework integrates a prescribed performance control approach with a recursive backstepping procedure, offering several key advantages: the capability to handle unmatched system uncertainties, the preservation of angle rigidity throughout the formation process, and the assurance that the triangulated formation shape is asymptotically achieved without risking collisions between neighboring agents. Furthermore, since the control input of each agent only requires local information related to its neighbors, which can be obtained locally from its own sensors, the proposed control method can be deployed in a communication-free environment. The effectiveness of the proposed control algorithms is validated by extensive numerical simulation.

Most existing prescribed performance formation control methods impose performance requirements on the consensus error rather than directly on the relative states between agents, which limits the physical interpretability of their solutions. This article proposes a novel adaptive prescribed performance formation control strategy that ensures prescribed performance of relative errors in uncertain high-order multiagent systems under both directed and undirected graphs. Since performance constraints are considered for relative errors, the error dynamics involve a coupled nonlinear interaction term that contains global graphical information among agents, making the design of a fully distributed control strategy more challenging. By proposing a series of nonlinear mappings and utilizing the edge Laplacian along with Lyapunov stability theory, the presented formation control scheme offers several advantages over existing approaches. Different performance requirements can be accommodated in a unified manner by solely tuning the design parameters a priori, eliminating the need for control redesign and stability reanalysis under the proposed fixed control protocol. This enhances user-friendliness and reduces implementation complexity. Furthermore, the verification process for the initial constraint, which is often complex and burdensome in existing prescribed performance control methods, is entirely avoided when the performance requirements are global. Additionally, the proposed approach fully decouples nonlinear interactions and ensures the asymptotic stability of the formation manifold through an adaptive parameter estimation technique. The effectiveness of the theoretical results is demonstrated through simulations.