This paper presents a novel control strategy for the compensation of the slippage effect during non-rigidly grasped object manipulation. A detailed dynamic model of the interconnected system composed of the robotic manipulator, the object and the internal forces and torques induced by the slippage effect is provided. Next, we design a model-based variable impedance control scheme, in order to achieve simultaneously zero convergence for the trajectory tracking error and the slippage velocity of the object. The desired damping and stiffness matrices are formulated online, by taking into account the measurement of the slippage velocity on the contact. A formal Lyapunov-based analysis guarantees the stability and convergence properties of the resulting control scheme. A set of extensive simulation studies clarifies the proposed method and verifies its efficacy.

In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.

MAVLink is a popular message protocol for small Unmanned Aerial Vehicles (UAVs). In this work, we present a Fault Detection and Isolation (FDI) framework for fixed-wing UAVs which takes advantage of the information conveyed in MAVLink telemetry streams and produces a bank of residual generators. Structural Analysis is employed to systematically handle the varying set of available measurements, identify the observable faults and adjust the FDI system accordingly. Structural detectability and isolability analyses are carried out. A case-study on a real-life telemetry log of a UAV crash demonstrates the efficacy of the proposed approach.

This paper presents the design of a vision-based object grasping and motion control architecture for a mobile manipulator system. The optimal grasping areas of the object are estimated using the partial point cloud acquired from an onboard RGB-D sensor system. The reach-to-grasp motion of the mobile manipulator is handled via a Nonlinear Model Predictive Control scheme. The controller is formulated accordingly in order to allow the system to operate in a constrained workspace with static obstacles. The goal of the proposed scheme is to guide the robot's end-effector towards the optimal grasping regions with guaranteed input and state constraints such as occlusion and obstacle avoidance, workspace boundaries and field of view constraints. The performance of the proposed strategy is experimentally verified using an 8 Degrees of Freedom KUKA Youbot in different reach-to-grasp scenarios.