This paper presents a novel decentralized control law for the Voronoi-based deployment of a Multi-Agent dynamical system. At each time instant, a bounded convex polyhedral working region is partitioned using a Voronoi algorithm providing the agents with non-overlapping functioning zones. The agents' deployment objective is to drive the entire system into a stable static configuration which corresponds to a stationary maximal coverage. This goal is achieved by using local stabilizing feedback control ensuring the convergence of each agent towards a centroid of its associated functioning zone. The proposed approach considers the Chebyshev center as centroidal point of each Voronoi cell.

This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping timevarying Voronoi cells associated to each UAV agent. The formation deployment objective is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. The proposed MPC-based formation reconfiguration algorithms allow not only faulty/non-cooperating agents to leave the formation, but also recovered/healthy agents to join in the current formation, while avoiding collisions. Simulation results validate the effectiveness of the proposed control algorithms.

This paper presents an improved approach for guaranteed state estimation combining set-membership estimation techniques based on zonotopes and ellipsoids, applied on linear discrete-time systems with unknown but bounded perturbations and noises. The proposed approach starts with a zonotopic approximation and continues with an ellipsoidal approximation; this allows to manage the trade-off between the accuracy of the zonotopic estimation and the reduced complexity of the ellipsoidal estimation. A new criterion based on the P -radius of a zonotope is proposed to make the transition from the zonotopic estimation to the ellipsoidal estimation. An illustrative example is analyzed to show the advantages of the proposed approach.

This paper proposes a novel strategy for completing a flight plan with a quadrotor UAV, in the context of aerial video making. The flight plan includes different types of waypoints to join, while respecting flight corridors and bounds on the derivatives of the position of the quadrotor. To this aim, non-uniform clamped B-splines are used to parameterize the trajectory. The latter is computed in order to minimize its overall duration, while ensuring the validation of the waypoints, satisfying the flight corridors and respecting the maximum magnitude on its derivatives. A receding waypoint horizon is used in order to split the optimization problem into smaller ones, which reduces the computation load when generating pieces of trajectories. The effectiveness of the proposed trajectory generation technique is demonstrated by simulation and through an outdoor flight experiment on a quadrotor.

This paper proposes a new ellipsoid-based guaranteed state estimation approach for linear discrete-time systems with bounded perturbations and bounded measurement noise. This approach is based on the minimization of the radius of the ellipsoidal state estimation set. Firstly, the ellipsoidal state estimation is computed by off-line solving a Linear Matrix Inequality optimization problem. Secondly, a new online method is developed in order to improve the accuracy of the estimation but it leads to an increase of the online computation load. A new scaling technique is proposed to reduce the computation time, while keeping a good accuracy of the state estimation. An illustrative example is analyzed in order to show the advantages of the proposed approach.

This paper presents a novel discrete-time decentralized control law for the Voronoi-based self-deployment of a Multi-Agent dynamical system. The basic control objective is to let the agents deploy into a bounded convex polyhedral region and maximize the coverage quality by computing locally the control action for each agent. The Voronoi tessellation algorithm is employed to partition dynamically the deployed region and to allocate each agent to a corresponding bounded functioning zone at each time instant. The control synthesis is then locally computed based on an optimal formulation framework related to the Lloyd's algorithm but according to the discrete-time agent's dynamics equation. The performance of the discretized optimal solution will be demonstrated via an illustrative example.

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This paper proposes a receding waypoint horizon strategy generating a piecewise polynomial trajectory with minimum jerk and predictive tracking of camera references for quadrotors, in the context of autonomous aerial singlesequence shots in a static environment. In order to deal with the limited on-board computation resources, the camera control is performed with an undersampled model predictive controller generating a set-point trajectory and a feedforward control signal, both used by a larger frequency controller. The performance of the overall strategy is illustrated with a real flight, on a Parrot Bebop 2 drone.