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This paper explores trajectory planning for robotic arms in 3D operational spaces. To improve the adaptation to dynamic via‐points in complex, confined environments, an enhanced dynamic movement primitives (DMP) approach is proposed and designed for dynamic planning under composite steering force field constraints. By incorporating steering attraction forces, this method enhances the generalization capability of DMP, allowing the robotic arm to navigate through dynamic via‐points flexibly without altering the start and end positions. The trajectory shape is adjusted via regression attraction forces, which helps preserve the demonstrated trajectory, reduce free‐space loss, and improve the system's adaptability to complex, dynamic environments. The convergence of the target state is rigorously proven using Lyapunov stability theory. Numerical simulations and experiments conducted with the Franka robotic arm validate the effectiveness of the proposed approach. Results show that in dynamic environments with multiple via‐points, this method produces reliable trajectories for robotic arm movements, significantly enhancing the adaptation of DMP to dynamic contexts. The planning process requires no additional learning, and the generated trajectory closely resembles the original demonstrated path. This method enables effective via‐point operations in confined spaces without requiring additional learning, while maintaining existing skills.

A one-dimensional (1D) virtual force field (VFF) algorithm for real-time reflexive local path planning of mobile robots is proposed. The 1D-VFF is composed of the virtual steering, obstacle and integrated force fields (IFFs). The steering force field (SFF) is generated by the local or global goal position. This SFF leads a mobile robot to the goal. The obstacle force field (OFF) is created by the raw data of a range measurement sensor (RMS). By this OFF, a mobile robot avoids obstacles. The IFF is produced by combining the steering and OFFs in which weights between 0 and 1 are multiplied. Through this IFF, a final steering command by which a mobile robot reaches a goal by avoiding obstacles is generated. Various simulations compare the performance of the proposed 1D-VFF with the weighted virtual tangential vector (WVTV), which is the recently suggested local path planning method to overcome the U-shaped enclosure problem.

This article describes some technical issues regarding the adaptation of a production car to a platform for the development and testing of autonomous driving technologies. A universal approach to performing the reverse engineering of electric power steering (EPS) for the purpose of external control is also presented. The primary objective of the related study was to solve the problem associated with the precise prediction of the dynamic trajectory of an autonomous vehicle. This was accomplished by deriving a new equation for determining the lateral tire forces and adjusting some of the vehicle parameters under road test conductions. A Mivar expert system was also integrated into the control system of the experimental autonomous vehicle. The expert system was made more flexible and effective for the present application by the introduction of hybrid artificial intelligence with logical reasoning. The innovation offers a solution to the major problem of liability in the event of an autonomous transport vehicle being involved in a collision.

In this paper, the impact of the magnetostriction mechanism is considered as the focus. An axisymmetric FEM model of the spiral‐coil electromagnetic acoustic transducers (EMAT) is established to conduct the simulation. The simulation results demonstrate that the directivity of ultrasonic wave can be controlled by manipulating the frequency. Furthermore, it is found that the direction of the dominant Lorentz force in the rail varies with time, while the magnetostrictive force compels the ultrasonic wave generated by the Lorentz force towards the axis. It effectively illustrates that the combined power of two mechanisms surpasses that of the Lorentz‐force mechanism alone, particularly at low frequencies. The leakage of the reflected energy of the ultrasonic wave generated by electromagnetic acoustic transducers (EMAT) is outside the receiving range and then weakens the amplitude of ultrasonic echo. To reduce the leakage of the reflected energy, this paper takes the impact of magnetostriction mechanism on frequency manipulation ultrasonic steering in EMAT, especially at low frequency.

Aiming at the high-precision torque output and saturation singularity avoidance problems in Lorentz force magnetic bearing (LFMB) swarms for magnetic levitation spacecraft, this study designs a manipulation law based on an adaptive weighted pseudo-inverse law. The system monitors each magnetic bearing’s working state in real time using high-precision position and current sensors. As the key input for the adaptive weighted pseudo-inverse control law, the sensor data’s measurement accuracy directly determines torque distribution effectiveness and attitude control precision. First, considering electromagnetic back-EMF effects, individual LFMB dynamics are modeled via the equivalent magnetic circuit method, with working principles elucidated. Subsequently, saturation coefficients for LFMB swarms are designed. Incorporating spacecraft maneuvering requirements, a genetic optimization algorithm establishes the optimal mounting configuration under task constraints. Considering the LFMB swarm configuration characteristics, this study proposes an adaptive weighted pseudo-inverse maneuvering law tailored to operational constraints. By designing an adaptive weighting matrix, the maneuvering law adjusts each LFMB’s torque output in real time, reducing residual saturation effects on attitude control speed and accuracy. Simulation results demonstrate that the proposed mounting configuration and adaptive weighted pseudo-inverse maneuvering law effectively mitigate saturation singularity’s impact on attitude control accuracy while reducing total energy consumption by 22%, validating the method’s effectiveness and superiority.

Achieving non-invasive spatiotemporal control over cellular functions, tissue organization, and behavior is a desirable aim for advanced therapies. Magnetic fields, due to their negligible interaction with biological matter, are promising for in vitro and in vivo applications, even in deep tissues. Particularly, the remote manipulation of paramagnetic (including superparamagnetic and ferromagnetic, all with a positive magnetic susceptibility) entities through magnetic instruments has emerged as a promising approach across various biological contexts. However, variations in the properties and descriptions of these instruments have led to a lack of reproducibility and comparability among studies. This article addresses the need for standardizing the characterization of magnetic instruments, with a specific focus on their ability to control the movement of paramagnetic objects within organisms. While it is well known that the force exerted on magnetic particles depends on the spatial variation (gradient) of the magnetic field, the magnitude of the field is often overlooked in the literature. Therefore, we comprehensively analyze and discuss both actors and propose a novel descriptor, termed ‘effective gradient’, which combines both dependencies. To illustrate the importance of both factors, we characterize different magnet systems and relate them to experiments involving superparamagnetic nanoparticles. This standardization effort aims to enhance the reproducibility and comparability of studies utilizing magnetic instruments for biological applications.

The idea of remote magnetic guiding is developed from the underlying physics of a concept that allows for bijective force generation over the inner volume of magnet systems. This concept can equally be implemented by electro- or permanent magnets. Here, permanent magnets are in the focus because they offer many advantages. The equations of magnetic fields and forces as well as velocities are derived in detail and physical limits are discussed. The special hydrodynamics of nanoparticle dispersions under these circumstances is reviewed and related to technical constraints. The possibility of 3D guiding and magnetic imaging techniques are discussed. Finally, the first results in guiding macroscopic objects, superparamagnetic nanoparticles, and cells with incorporated nanoparticles are presented. The constructed magnet systems allow for orientation, movement, and acceleration of magnetic objects and, in principle, can be scaled up to human size.

Model Predictive Control (MPC) predicts the vehicle’s motion within a fixed time window, known as the prediction horizon, and calculates potential collision risks with obstacles in advance. It then determines the optimal steering input to guide the vehicle safely around obstacles. For example, when a sudden obstacle appears, sensors detect it, and MPC uses the vehicle’s current speed, position, and heading to predict its driving trajectory over the next few hundred milliseconds to several seconds. If a collision is predicted, MPC computes the optimal steering path among possible avoidance trajectories that are feasible within the vehicle’s dynamics. The vehicle then follows this input to steer away from the obstacle. In the proposed method, MPC is combined with Adaptive Artificial Potential Field (APF). The APF dynamically adjusts the repulsive force based on the distance and relative speed to the obstacle. MPC predicts the optimal driving path and generates control inputs, while the avoidance vector from APF is integrated into MPC’s constraints or cost function. Simulation results demonstrate that the proposed method significantly improves obstacle avoidance response, steering smoothness, and path stability compared to the baseline MPC approach.

A magnetic-controlled flexible continuum robot (MFCR) is a kind of continuum robot with small-size and flexibility that deforms under controlled magnetic fields, which makes MFCRs easy to fit in special sizes and designs and provides them with the ability to feasibly arrive at the desired area through certain blood vessel bifurcation. The magnetic drive method is suitable for the miniaturization of soft continuum robots but shows limitations in realizing high flexibility. To achieve miniaturization and high flexibility, in this work, the deformation schemes of a magnetic-controlled flexible continuum robot (MFCR) are proposed, simulated, and experimentally validated. The proposed MFCR includes a soft steering part made of a silicone elastomer with uniformly dispersed NdFeB powder which has a specific magnetization direction. With the actuation of different magnetic fields, the proposed MFCR shows three different deformation modes (C-shape, J-shape, and S-shape) and high flexibility. By using the potential energy model combined with magnetic and elastic potential energy, the quasi-static deformation model of MFCR is built. Through various simulations and experiments, we analyzed and predicted different deformation modes. The results from the experiments demonstrate the accuracy of the deformation model. The results indicate that the MFCR has good control precision and deformation performance with potential applications in robot-assisted minimally invasive surgery.

Drilling trajectory closed-loop control in directional drilling is a key technology for achieving high-precision drilling. However, due to the complex geological conditions, and engineering limitations of drilling tools, traditional control methods of drilling often face challenges, such as error accumulation, response delays, and control instability. To address these issues, this paper proposes an intelligent closed-loop steering method based on online adaptive optimization. The core of this method lies in the construction of an integrated “perception–optimization–execution” intelligent steering framework. First, real-time attitude feedback is used to accurately perceive trajectory deviations. Then, an optimization model is triggered, aiming to minimize deviations under the dogleg severity constraint, and genetic algorithms are employed to dynamically calibrate the PID controller online, effectively eliminating error accumulation. Finally, based on the optimization results, real-time calculations of tool face angle and steering tool force are performed to ensure precise execution of steering commands. Simulation results show that, compared to the traditional PID and PID-APF methods, the proposed method demonstrates significant advantages in trajectory control accuracy and wellbore quality. Under noise-free conditions, the normal distance accuracy improves by 88.89% and 34.02%, respectively, and dogleg severity is reduced by 6.30% and 5.81%. Under noise interference, the normal distance accuracy improves by 56.73% and 54.97%, respectively, and dogleg severity is reduced by 23.38% and 4.85%. In conclusion, the proposed intelligent closed-loop control method not only significantly enhances the system’s real-time response capability and control precision but also exhibits stronger robustness, with broad potential for engineering applications.