This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach $\mathrm{0.61\; mm}$ and $$0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of $10.2$ mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

Compliant force control is a fundamental capability for enabling robots to interact safely and effectively with dynamic and uncertain environments. This paper presents a comprehensive survey of compliant force control strategies, intending to enhance safety, adaptability, and precision in applications such as physical human–robot interaction, robotic manipulation, and collaborative tasks. The review begins with a classification of compliant control methods into passive and active approaches, followed by a detailed examination of direct force control techniques—including hybrid and parallel force/position control—and indirect methods such as impedance and admittance control. Special emphasis is placed on advanced compliant control strategies applied to structurally complex robotic systems, including aerial, mobile, cable-driven, and bionic robots. In addition, intelligent compliant control approaches are systematically analyzed, encompassing neural networks, fuzzy logic, sliding mode control, and reinforcement learning. Sensorless compliance techniques are also discussed, along with emerging trends in hardware design and intelligent control methodologies. This survey provides a holistic view of the current landscape, identifies key technical challenges, and outlines future research directions for achieving more robust, intelligent, and adaptive compliant force control in robotic systems.

This paper develops a direct analytical framework for synchronizing and controlling fractional-order octonion-valued fuzzy bidirectional associative memory neural networks (FOOVFBAMNNs). Octonion algebra is neither commutative nor associative, which limits the application of standard analytical tools. To address this challenge, we first propose a generalized Cauchy–Schwarz inequality tailored to the octonionic domain, which operates directly without relying on system decomposition. This inequality lays the groundwork for a Lyapunov-based stability analysis that retains the system’s inherent geometric structure to avoid decomposition into real-valued components. Based on this framework, we derive concise 2-norm inequality criteria, which are sufficient to guarantee Mittag-Leffler synchronization of the proposed model. We also employ a Particle Swarm Optimization (PSO) algorithm to systematically optimize the flexible parameters in the generalized inequality, enhancing the practical performance of the synchronization scheme. To validate the effectiveness of the proposed method, we apply it to a multi-domain image restoration task. Numerical experiments verify the performance of our method. In terms of Peak Signal-to-Noise Ratio (PSNR), the octonion-valued network with PSO-tuned parameters achieves better results than its non-optimized counterpart as well as models constructed in complex or quaternion domains.

Mobile robots have been widely engaged in many fields. To obtain the precise and consistent localization of mobile robots, the Global Navigation Satellite System (GNSS) is often employed. With the continuous development and modernization of GNSS, more tracked satellites can be used for multi-GNSS positioning calculation, which can improve the positioning performance and enhance accuracy. However, it also increases computational complexity. Therefore, a satellite selection method, which selects a subset from all visible satellites, is necessary. In multi-GNSS positioning, the geometric dilution of precision (GDOP) is an essential metric for satellite selection. However, the traditional traversal method requires a large amount of solution resources. In this paper, we proposed an improved genetic algorithm for satellite selection. By defining the maturity factor (MF) to guide the crossover and mutation operators, the search performance is guaranteed while reducing unnecessary crossover and mutation operations, thus reducing the search time. By adopting the previous epoch optimal individual inheritance strategy, the satellite selection results of subsequent epochs under continuous epochs have been improved. The experimental results verify the effectiveness of the proposed method.

In this article, an adaptive vision-admittance-based leakage-type (LT) sliding mode control (SMC) is proposed to solve the uncertain robots’ compliant force control problem with actuator saturation. First, we design the vision-admittance-based model to generate an online reference trajectory concerning the predefined task trajectory and the interaction force command in the image feature space. Second, an adaptive SMC (ASMC) is created to track the generated reference image feature trajectory with high precision. An LT adaptive control law is applied in this controller to overcome the system uncertainties’ impacts in the situation where the upper bound of uncertainty is unknown. Moreover, an auxiliary dynamic term is taken into account in this controller’s design to solve the saturation nonlinearity problem. The feature tracking error can be guaranteed to be convergent according to the Lyapunov stability analysis. Finally, prototype experiments are conducted on a 6-degree-of-freedom (DOF) STEP SD7/700 robot to validate the superiority of the proposed control methodology.