Path planning and formation control are both challenging and critical issues in robotics, which involve computing an optimal path from the initial position to target while keeping the desired formation. This brief studies the path planning and formation control problem for multiple unmanned aerial vehicles (multi-UAVs) in 3-D constrained space. Considering the local minimum of artificial potential function (APF), an effective improved artificial potential function (IAPF) based path planning approach is proposed for the multi-UAV systems. By introducing a rotating potential field, the UAVs can escape from the common local minimum and oscillations efficiently. Afterwards, by using the leader-follower model, a formation controller based on potential function method is developed to ensure that the follower UAVs keep the desired angles and distances with the leader, and a Lyapunov function is designed to analyze the closed-system stability. Finally, simulation studies under different environmental constraints confirm the efficiency of the proposed approaches in addressing the path planning and formation control issues in 3-D space.

In this article, we address the distributed optimal coordinated control (DOCC) problem for unmanned surface vehicles (USVs) suffering from limited network channels and communication interruptions. For networked USVs, ignoring network limitations can cause serious sailing incidents. Therefore, we developed a DOCC design with a limited number of communication channels and a restricted communication range. The proposed optimal control system includes two subsystems: coordinated trajectory optimization and local trajectory tracking control. The first employs a delicately designed auxiliary system to determine the optimal solution for each USV. To address the scarcity and intermittency of communication channels, we also develop an intermittent interleaved periodic event-triggered mechanism (IIPETM) in the auxiliary system. The second subsystem adopts a neural network-based backstepping approach to achieve the tracking objective. Compared with the existing methods, the developed algorithm can interleave the triggering instants for different USVs and ensure system stability, even under communication interruptions. We demonstrate the effectiveness and superiority of the proposed scheme through numerical simulations of USVs.