Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot’s dynamics model, and propose a nonlinear model predictive control (NMPC)-based trajectory tracking control algorithm. Compared against state-of-the-art, the contribution is threefold: First, the combination of three-wheeled omnidirectional locomotion and non-contact negative pressure air chamber adhesion achieves omnidirectional locomotion on non-ferromagnetic vertical surfaces. Second, the critical slip state has been employed as an acceleration constraint condition, which could improve the maximum linear acceleration and the angular acceleration by $164.71\%$ and $22.07\%$ on average, respectively. Last, an NMPC-based trajectory tracking control algorithm is proposed. According to the simulation experiment results, the tracking accuracy is higher than the traditional PID controller.

Unmanned aerial vehicles (UAVs) possess fast-moving abilities and have been used in various tasks in the past decades. However, their performances are still restricted by insufficient endurance and confined environments. Intuitively, combining other locomotion modes with UAVs, such as diving and driving, would be an appropriate idea to improve the robot’s adaptability and solve the endurance problem. Recently, the terrestrial/aerial hybrid robots have drawn the researchers’ eyes for their outstanding performances, which can deploy flight mode to traverse insurmountable terrains and ground mode to increase endurance and realize detailed searches. Therefore, this paper developed the autonomous quadrotor tilting hybrid robot (AQT-HR) to achieve terrestrial/aerial dual-modal mobility and verified that the robot delivers high energy efficiency. The AQT-HR can achieve flying and driving through a quadrotor tilting mechanism, which can alter one single driving force into different directions. Furthermore, the dynamic models of the hybrid robot’s aerial and ground locomotion are derived and introduced into the model-feedforward PID control algorithm for improving the robot’s flying stability. Finally, we conducted some mobility tests and experiments about traversing obstacles to demonstrate that the proposed hybrid robot can realize autonomous mode switching and perform a low energy consumption in ground movement mode.

Frequency domain spline adaptive filters (FDSAF) have attracted a great deal of attention because of their enhanced computational efficiency. However, their performance will be extremely degraded in practical environments where non-Gaussian phenomena exist. In order to attain good robustness to non-Gaussian noises while ensuring a low computational burden, this paper proposes a frequency domain spline adaptive filtering algorithm based on the half-quadratic criterion (HQC), named FDSAF-HQC. Further, to offer a theoretical foundation for the proposed FDSAF-HQC, a detailed performance analysis based on the principle of energy conservation is conducted and then validated by simulations. Moreover, the computational complexity of FDSAF-HQC is evaluated and compared with other similar algorithms. Simulation results of nonlinear system identification corroborate the outstanding performance of the proposed FDSAF-HQC, i.e., better robustness and computational efficiency. The superiority of the proposed FDSAF-HQC is also supported by practical applications of echo cancellation and biological signal denoising.

Spline prioritization optimization adaptive filter (SPOAF) based on maximum Versoria criterion (MVC), named SPOAF-MVC, is renowned for its excellent robustness and low steady-state error. However, there is still room for improvement in its convergence speed, and its mean-square performance analysis has not yet been implemented. In view of the above, this brief proposes a robust SPOAF based on a modified adaptive moment estimation (MAME) method, called SPOAF-MVC-MAME, and makes a detailed theoretical analysis of the algorithm’s performance. Simulation results provide practical evidence that bolsters the theoretical analysis and verify the efficacy of our algorithm.

The primary challenge facing the field of magnetic anomaly detection (MAD) currently lies in how to effectively improve detection performance in low signal-to-noise ratio (SNR) and real 1/fα noise scenarios. To overcome these difficulties, this article proposes an optimized MAD method based on random forest (RF) classifier. This method utilizes an orthonormal basis function (OBF) detector based on Karhunen-Loève expansion (KLE) to extract energy from the raw data as time domain features. Meanwhile, the spectral information derived from lowpass filtering (LPF) and fast Fourier transform (FFT) serves as frequency domain features of the raw data. The cutoff frequency of the LPF is determined based on a frequency characteristic function that defines the high-frequency boundary of the target signal. Combining these time and frequency domain features, a simulated dataset is constructed for the training and testing of the detection model. Subsequently, the trained model undergoes further validation and evaluation on semi-real and real datasets built upon measured data from a tunnelling magnetoresistance (TMR) magnetic sensor. Through a series of simulations, we demonstrate that our designed method exhibits superior detection capability and stronger generalization ability compared to other similar OBF-based methods. Furthermore, the superiority of this method is also confirmed by experimental results based on actually measured data.