This article focuses on the modeling and controlling of Unmanned Aerial Manipulators (UAMs) in a leader/follower configuration performing a cooperative manipulation task. Each UAM consists of an Unmanned Aerial Vehicle (UAV) with an attached serial-link robotic manipulator. The Recursive Newton-Euler dynamics formulation is employed to account for the interaction between the UAV and its manipulator. The overall system consists of a couple UAMs with a carrying load. The coupling between these systems is due to the exerted forces by their manipulators through the object characterized by its stiffness matrix. A leader/follower control scheme is employed with a stability-analysis tailored to the UAM-pair. The leader UAM defines the trajectory of the moving object while the follower acts so as to reduce the system's internal reaction forces. Simulation studies are employed to validate the controller's performance while comparing the system's response against that derived from a classical nonlinear tracking controller.

This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about $0.02$ m.