Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.

A modular reconfigurable robot (MRR) is considered as a system of systems; that is, a robot control system consists of modular systems. For an MRR, onboard tracking of multiple objects is one of the most challenging tasks. This is because the task requires lots of computation resources, but an MRR system is constrained by several physical limitations. To enhance the computing performance of an MRR control system, a novel approach to organizing a distributed computing system is proposed. The key components of the proposed approach are FPGA-based specialized computational cores (SCCs). An SCC for tracking objects is designed to utilize spare resources of the MRR module's control devices. Analysis and experiments on a real testbench are performed to illustrate that the proposed approach can enhance overall computing performance of an MRR control system without adding additional components. As such, weight and dimensional parameters are saved; unnecessary power consumption and heat generation are reduced. One experimental example shows that the proposed SCC enables a Kalman filter to track objects up to 100 times faster than a comparable solution implemented with microcontrollers, while the whole distributed computing system can outperform a similar one built on high-performance CPUs.

Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of the robot. The objectives can vary with different weights depending upon the situation, reflecting relative objective importance. This study developed a multiobjective reinforcement learning algorithm based on a fuzzy inference system (FI-MORL) to select the most appropriate gait parameters of snake-like MRs according to the objective weights. The developed algorithm employs a fuzzy inference system to reduce the number of states in an environment, which results in faster learning. The proposed approach uses the previously learned experience to rapidly achieve the best objective values in response to a change in weights. While setting equal importance to the objectives, FI-MORL delivers superior performance than single-objective reinforcement learning algorithms by consuming 2% less power and gaining 2.5% higher velocity since it mitigates the effect of weight change, similar performance found comparing an actor-critic algorithm. Likewise, the proposed method outperforms by consuming 14% less power and achieving 11% higher velocity than traditional methods like proximal policy optimization, deep Q-network, and vanilla policy gradient. Even after weight change, FI-MORL achieved a 14% higher reward than the above methods. The proposed FI-MORL framework can effectively converge quicker and efficiently handle the changes in objective weights.

The paper proposes a new approach for FPGA reconfiguration based on usage of the open-source synthesis and place-and-route tools. The well-known partial reconfiguration technologies assume pre-compilation of all required FPGA configurations by the designer. In autonomous intelligent control systems it can become quite a strong limitation, due to control law couldn't be changed on-board without using external FPGA development tool. The paper proposes automatic Verilog code generation and synthesis directly on embedded controller, followed by full FPGA reconfiguration. The experimental results shows, that the resource usage of the FPGA configurations built by open-source tools are comparable to the ones built by well-known commercial toolchain).

This study proposes a heterogeneous modular reconfigurable robot called SABER (short for Step, Assembler, and Bridge Explorer Robot) that is suitable for future autonomous extraterrestrial missions. SABER comprises a platform and a reconfigurable rail and can operate in monowheel, rail trolley, and manipulator configurations. The monowheel configuration provides locomotion at a top speed of 10 km/h. The rail trolley configuration allows the robot to traverse gaps whose length is greater than its wheel diameter, climb steps whose height is no taller than the wheel radius, and pass through passages narrower than the wheel diameter. The manipulator configuration enables the robot to manipulate objects using two robotic arms with changeable tools. This design allows several SABERs to work as a team to enlarge their working area or enhance their mechanical capabilities. As such, SABERs can undertake various tasks in extraterrestrial missions, including exploration, outpost building and equipment maintenance. The key feature of the SABER is its mechanical transmission that allows its platform to move rapidly along a rail made from the manipulators of the robot. We also formulated the novel concept of a jamming safety diagram that is used to determine controller precision requirements to prevent the transmission from jamming. Finally, a new positioning controller meeting those requirements was introduced and implemented as field-programmable-gate-array firmware that is compatible with a wide range of low-cost integrated circuits. In numerical simulations, we evaluated SABER with respect to its speed, acceleration, oscillation, and ability to negotiate gaps and steps; SABER performed as well as or better than existing autonomous mobile robots and modular reconfigurable robots.

This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F₁-score, was estimated to be 83%.