Trust between sensor nodes is very essential to improve security, dependability, energy efficiency, scalability, and cooperation in Industrial Wireless Sensor Networks (IWSNs). In order to enhance cooperation and security on a large scale in IWSNs, we suggest a Trust-based Integrated Techniques for Anomaly Neutralization (TITAN), where unequal clustering can be used to detect and shrink unacceptable sensor nodes and save resources. Disparate strategic clustering helps in boosting energy efficiency through the creation of smaller clusters close to the sink and larger ones at the distances, therefore encouraging the more tolerable distribution of power and communication encumbrance. Heads of the clusters are dynamically selected depending on the fitness of the nodes within the cluster through a new Optimal Cluster Representative Election Algorithm (OCREA). The fitness of a node is based on its residual power, connection quality, signal strength and distance to the sink. TITAN applies distributed intra-cluster trust to make decisions combining with centralized inter-cluster methodologies, merging attack-resistant trust evaluations and effective trust aggregation. In addition, TITAN applies an appealing dynamic logarithmic trust fund distribution of rewards and sanctions based on the actions of sensor nodes, it is possible to distinguish between reliable and faulty nodes. Also, it includes key indicators of communication trust, data trust, and energy measurement to allow proper trust measurement. TITAN considers a dynamic aging factor and damping factor, which ensures that reliability of sensor nodes is considered on account of recent exchanges, and, therefore, minimizing the influence of old information. To a larger extent, the model incorporates a logarithmic penalty term that punishes the node when the rate of unsuccessful interactions goes up hence effectively isolating with untrustworthy nodes. TITAN enhances better and reliable and robust trust assessment as it incorporates feedback provided by trustworthy neighbor’s nodes and the manipulation of trust levels using an extensive analysis. The combination of these features contributes to the overall performance and improves it’s security of IWSNs, which enables them to be better resistant to attacks and use less resource to run in resource-constrained environments. Due to its communication overhead, trust evaluation and detected malicious nodes, the solution proposed is superior in its capabilities compared to other solutions authenticated with extensive simulations. TITAN manages to recognize the presence of the malicious nodes with 87 percent even when the malicious nodes are less than 60 percent, meaningfully better than such comparative models as SDTS and DTMS. The positive error rate and the negative error rate is minimized with a precision in detection increased to 9% and 6% respectively. TITAN also maintains high packets delivery ratio of above 89% and reduced the average packet loss to only 36 as compared to more than 60 in the baseline schemes. Also, the energy consumption is lowered by about 14% that confirms the effectiveness of TITAN. These findings all indicate the strength and scalability of TITAN Energy-constrained environment performance and threat-prone IWSN environments performance.

Quadratic Programs (QPs) are widely used in the control of walking robots, especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In both cases, the controller design requires the formulation of a QP and the selection of a suitable QP solver, both requiring considerable time and expertise. While computational performance benchmarks exist for QP solvers, studies comparing optimal combinations of computational hardware (HW), QP formulation, and solver performance are lacking. In this work, we compare dense and sparse QP formulations, and multiple solving methods on different HW architectures, focusing on their computational efficiency in dynamic walking of four-legged robots using MPC. We introduce the Solve Frequency per Watt (SFPW) as a performance measure to enable a cross-hardware comparison of the efficiency of QP solvers. We also benchmark different QP solvers for WBC that we use for trajectory stabilization in quadrupedal walking. As a result, this paper recommends a starting point for practitioners on the selection of QP formulations and solvers for different HW architectures in walking robots and indicates which problems should be devoted the greater technical effort.

Many real-world applications require legged robots to be able to carry variable payloads. Model-Based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime.

In this paper, we present an attitude control scheme for an autonomous underwater vehicle (AUV), which is based on incremental nonlinear dynamic inversion (INDI). Conventional model-based controllers depend on an exact model of the controlled system, which is difficult to find, especially for marine vehicles subject to highly nonlinear hydrodynamic effects. INDI trades off model accuracy with sensor accuracy by incorporating acceleration feedback and actuator output feedback to linearize a nonlinear system incrementally. Existing research primarily focuses on studying INDI on unmanned aerial vehicles. However, there is barely any research on controlling marine vehicles using INDI. The control task we are performing is a 90 degrees pitch-up maneuver, where the dual-arm intervention AUV Cuttlefish transitions from a horizontal traveling pose to a vertical intervention pose. We compare INDI to a classical model-based control scheme in the maritime test basin at DFKI RIC, Germany, and we find that INDI keeps the AUV much more steady both in the transitioning phase as well as in the station keeping phase.

Musical dancing is an ubiquitous phenomenon in the human society. Providing robots the ability to dance has the potential to make the human robot co-existence more acceptable in our society. Hence, dancing robots have generated a considerable research interest in the recent years. In this paper, we present a novel formalization of robot dancing as planning and control of optimally timed actions based on beat timings and additional features extracted from the music. We showcase the use of this formulation in three different variations: with input of human expert choreography, imitation of a predefined choreography, and automated generation of a novel choreography. Our method has been validated on four different musical pieces, both in simulation and on a real robot, using the upper-body humanoid robot RH5 Manus.

Space robotics applications, such as Active Space Debris Removal (ASDR), require representative testing before launch. A commonly used approach to emulate the microgravity environment in space is air-bearing based platforms on flat-floors, such as the European Space Agency's Orbital Robotics and GNC Lab (ORGL). This work proposes a control architecture for a floating platform at the ORGL, equipped with eight solenoid-valve-based thrusters and one reaction wheel. The control architecture consists of two main components: a trajectory planner that finds optimal trajectories connecting two states and a trajectory follower that follows any physically feasible trajectory. The controller is first evaluated within an introduced simulation, achieving a 100% success rate at finding and following trajectories to the origin within a Monte-Carlo test. Individual trajectories are also successfully followed by the physical system. In this work, we showcase the ability of the controller to reject disturbances and follow a straight-line trajectory within tens of centimeters.