A reliable communication network is essential for multiple UAVs operating within obstacle-cluttered environments, where limited communication due to obstructions often occurs. A common solution is to deploy intermediate UAVs to relay information via a multi-hop network, which introduces two challenges: (i) how to design the structure of multi-hop networks; and (ii) how to maintain connectivity during collaborative motion. To this end, this work first proposes an efficient constrained search method based on the minimum-edge RRT⋆algorithm, to find a spanning-tree topology that requires a less number of UAVs for the deployment task. Then, to achieve this deployment, a distributed model predictive control strategy is proposed for the online motion coordination. It explicitly incorporates not only the inter-UAV and UAV-obstacle distance constraints, but also the line-of-sight (LOS) connectivity constraint. These constraints are well-known to be nonlinear and often tackled by various approximations. In contrast, this work provides a theoretical guarantee that all agent trajectories are ensured to be collision-free with a team-wise LOS connectivity at all time. Numerous simulations are performed in 3D valley-like environments, while hardware experiments validate its dynamic adaptation when the deployment position changes online.

Long-term monitoring of numerous dynamic targets can be tedious for a human operator and infeasible for a single robot, e.g., to monitor wild flocks, detect intruders, search and rescue. Fleets of autonomous robots can be effective by acting collaboratively and concurrently. However, the online coordination is challenging due to the unknown behaviors of the targets and the limited perception of each robot. Existing work often deploys all robots available without minimizing the fleet size, or neglects the constraints on their resources such as battery and memory. This work proposes an online coordination scheme called LOMORO for collaborative target monitoring, path routing and resource charging. It includes three core components: (I) the modeling of multi-robot task assignment problem under the constraints on resources and monitoring intervals; (II) the resource-aware task coordination algorithm iterates between the high-level assignment of dynamic targets and the low-level multi-objective routing via the Martin's algorithm; (III) the online adaptation algorithm in case of unpredictable target behaviors and robot failures. It ensures the explicitly upper-bounded monitoring intervals for all targets and the lower-bounded resource levels for all robots, while minimizing the average number of active robots. The proposed methods are validated extensively via large-scale simulations against several baselines, under different road networks, robot velocities, charging rates and monitoring intervals.

Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discrete actions while neglecting the dynamic constraints of the robots and unknown behaviors of the targets. This work proposes an online task and motion coordination algorithm that ensures an explicitly-bounded estimation uncertainty for the target states, while minimizing the average number of active robots. The robots have a limited-range perception to actively track a limited number of targets simultaneously, of which their future control decisions are all unknown. It includes: (i) the assignment of monitoring tasks, modeled as a flexible size multiple vehicle routing problem with time windows (m-MVRPTW), given the predicted target trajectories with uncertainty measure in the road-networks; (ii) the nonlinear model predictive control (NMPC) for optimizing the robot trajectories under uncertainty and safety constraints. It is shown that the robots can switch between active and inactive roles dynamically online as required by the unknown monitoring task. The proposed methods are validated via large-scale simulations of up to 100 robots and targets.

This work is concerned with dynamic output feedback control for a class of switched systems with mode-dependent average dwell time switching. By constructing a quasi-time-dependent Lyapunov function, the issues of global uniform exponential stability and ℓ2-gain analysis for the switched system are addressed firstly. Then a set of reduced-order output feedback controllers is designed, which is both mode-dependent and quasi-time-dependent. An example is presented to verify the effectiveness of the proposed method.