This paper presents a comparative analysis of state estimation techniques, SDRE-based Kalman and H-infinity filters, ${\mathcal{H}_\infty }$, within the context of nonlinear systems controlled via the State-Dependent Riccati Equation (SDRE) framework. While the SDRE-based Kalman filter is effective under Gaussian noise assumptions, it can underperform in environments with non-Gaussian disturbances. The SDRE-based ${\mathcal{H}_\infty }$ filter, on the other hand, offers a robust alternative by minimizing the worst-case estimation error without assuming any noise statistics. To evaluate both filters, we conduct simulation studies on two real-world scenarios: temperature control in a data center and interference mitigation in a wireless cellular network. Results demonstrate that the ${\mathcal{H}_\infty }$ filter outperforms its Kalman counterpart in the presence of non-Gaussian noise, validating its suitability for robust estimation in practical applications.

This paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

In this paper, we present a method for depth control of one degree of freedom (1DOF) underwater robotic platform aMussel, based on the measurements from the ambient light sensor. Since ambient light values change during the day and depend on the weather conditions, references for the controller are acquired from other aMussel holding depth using pressure sensor based controller. Control inputs are transmitted using acoustic communication.

In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle's private zone, i.e., the pre-allocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups-a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.