Hua Zhu

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.

In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.

Friction and wear is one of the important energy losses of cylinder liner piston rings (CLPRs). The research on the design of running in test parameters of cylinder liner and piston ring is of great significance to reduce the energy loss of internal combustion engine and improve the service life. In order to shorten the running-in time and improve the running-in quality of CLPR, the running-in tests were carried out and running-in parameters of CLPR were designed based on running-in attractor theory. The running-in tests of CLPR samples were carried out for 28 groups under different speed and load conditions, and the friction signal and the running-in surface morphology were collected. The running-in attractor phase trajectory is constructed from the friction signal time series, and the characteristic parameters of the running-in attractor are calculated. The running-in state and time under different working conditions are identified by the evolution law of the running-in attractor phase trajectory and chaotic characteristic quantities. The running-in quality under different working conditions is obtained by the fractal dimension of the surface morphology and characteristic roughness parameters. The running-in parameters of CLPR aiming at both short running-in time and improve running-in quality are designed, and a multi index optimization design aiming at short running-in time and good running-in quality is carried out. The results show that an optimal solution for the CLPR running-in tests under different objectives does existed, and the corresponding optimal running-in parameters are obtained. These parameters, in turn, guide the choice of optimal working conditions. This work can provide ideas for the study of other friction pairs.

In this study, the characterization method and evolution law of phase-point saturation based on the capacity dimension in fractal theory are studied to investigate the running-in process and state identification of a system. Tests of the running-in process under different working conditions were conducted on a ring-disc tribometer, and the friction signals were collected. The phase trajectories in different stages were then constructed and the fractal dimensions were calculated. The phase-point saturation parameter is proposed to reflect the degree of saturation of a phase space filled with phase points, and its reliability, noise-resistance, correlation with the fractal dimension, and effectiveness of the characterization of the running-in process were thoroughly investigated. The results demonstrate that the proposed phase-point saturation parameter is reliable and resistant to noise and can be used to identify changes in the system state. Furthermore, there is a negative correlation between the phase-point saturation parameter and the fractal dimension, and the phase-point saturation parameter can effectively characterize the state characteristics and describe the running-in process. During the running-in process, the phase-point saturation changes from high to low and tends to be stable when it reaches the running-in state. When entering the stable wear stage, the value of the parameter is maintained near the minimum, it fluctuates little as compared with the correlation dimension, and it exhibits good stability. Therefore, the proposed phase-point saturation parameter has important theoretical significance and application value for the quantitative characterization of the running-in process and state identification.

Numerous missions in both civil and military fields involve the pursuit-evasion problem of vehicles. With vertical take-off and landing capability, the intelligent air–ground vehicle expands its feasible path to 3-D space, which has great advantages in the pursuit. This vehicle requires adequate path planning to obtain an optimal 3-D path and further improve the pursuit efficiency. The planning process of the air–ground vehicle currently faces the technical difficulties of acquiring the proper takeoff timing and position while optimizing the planning trajectory, especially in a complex environment with dense obstacles. To solve the above issues, a game-learning-based smooth path planning strategy for the intelligent air–ground vehicle considering mode switching is proposed in this article. First, a new reward function of the $Q$ -learning algorithm, considering the influence of flight obstacle crossing parameters, is presented to explore the short forward track distance. Second, in the update rule, the pursuit-evasion game acts in the mode switching decisions. During interactive learning between the vehicle and environment, this game constantly updates the Nash equilibrium solutions for mode switching and gets a series of switching decisions of the pursuer vehicle (ego vehicle). Third, a double-yaw correction for path smoothing modification is proposed to reduce turning points and avoid local path deviations. This modification provides heuristic information for the exploration of the environment, which significantly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on a 1000 m*1000 m map with 0–200 m obstacle height. Results show that this strategy is effective to decrease the 253-m distance compared with the traditional reinforcement learning algorithm and has a faster convergence speed. The number of trajectory direction changes is 36% less than that of the game-learning algorithm only considering mode switching. The unreasonable large angle turns are eliminated.

Several pairs of algorithms were used to determine the phase space reconstruction parameters to analyze the dynamic characteristics of chaotic time series. The reconstructed phase trajectories were compared with the original phase trajectories of the Lorenz attractor, Rössler attractor, and Chen's attractor to obtain the optimum method for determining the phase space reconstruction parameters with high precision and efficiency. The research results show that the false nearest neighbor method and the complex autocorrelation method provided the best results. The saturated embedding dimension method based on the saturated correlation dimension method is proposed to calculate the time delay. Different time delays are obtained by changing the embedding dimension parameters of the complex autocorrelation method. The optimum time delay occurs at the point where the time delay is stable. The validity of the method is verified by combing the application of the correlation dimension, showing that the proposed method is suitable for the effective determination of the phase space reconstruction parameters.

In the current study, we propose a bifractal stratified characterization method to evaluate the surface profile evolution of a plateau honing cylinder liner during the wear process. This method is suitable for the characterization of two-process and worn surface profiles with bifractal and stratified characteristics. We develop a bifractal truncated separation structure function method to calculate the characterization parameters of the proposed method. In particular, the upper- and lower-strata surface profiles are truncated and separated in order to calculate their fractal parameters. Experimental results demonstrate the ability of the proposed method to outperform the traditional fractal method in characterizing the evolution of the plateau honing cylinder liner surface profile during the wearing process.

In this study, the coefficient of friction (COF) signals throughout the running-in process were examined by sliding a ring against a static disc. By reconstructing the scalar time-series into multi-dimensional phase spaces, friction-induced attractors were obtained and quantified by recursive characteristics analysis, which can effectively realize the running-in status identification. Moreover, a recursive characteristics analysis-based evaluation model was established to investigate the stationarity of the friction-induced attractors based on the recurrence quantification analysis (RQA) measures. The analyses of the numerically simulated signals and experimental results indicate that the extracted model is an intuitive and effective method. Furthermore, to improve the stationary of the friction-induced attractors, the normal pressure should be low, whereas the relative sliding velocities should be increased appropriately. These results would contribute to the revelation of the recursive characteristics of the tribosystem and the improvement of the stationarity of friction-induced attractors.

This paper presents a modified complete normal contact stiffness model of a fractal surface considering contact friction. We use this model to study the influence of fractal dimensions and fractal roughness on normal contact stiffness. The fractal micro-contact model of an asperity and the complete length scale contact model of fractal surface (both contrasting classical mechanics) are revised. The influence of frictional resistance at micro-contact interfaces on normal contact stiffness is also considered. Predictions of the new model are found to be in greater agreement with the results of the experiments than the predictions of the original model. The study analyzes the influence of fractal dimensions and fractal roughness on the normal contact stiffness. With the increase of these two fractal parameters, their influences on the normal contact stiffness are opposite and are different under high pressure and low pressure.