Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.

The cutting sound signal of a coal mining shearer is an important signal source for identifying the coal–rock cutting mode and load state. However, the coal–rock cutting sound signal directly collected from the industrial field always contains a large amount of background noise, which is not conducive to the subsequent feature extraction and recognition. Therefore, efficient noise elimination for the original signal is required. An intelligent processing method based on an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) denoising algorithm is constructed for the cutting sound signal in this paper. CEEMDAN first decomposes the sound to generate a series of intrinsic modal functions (IMFs). Because the denoising threshold of each IMF is usually obtained by an experimental test or an empirical formula in the traditional CEEMDAN method, obtaining an optimal threshold set for each IMF is difficult. The processing effect is often restricted. To overcome this problem, the fruit fly optimization algorithm (FOA) was introduced for CEEMDAN threshold determination. Moreover, in the basic FOA, the scouting bee mutation operation and adaptive dynamic adjustment search strategy are applied to maintain the convergence speed and global search ability. The simulation result shows that the signal waveform processed by the improved CEEMDAN denoising algorithm is smoother than the other four typical eliminate noise signal algorithms. The output signal’s signal-to-noise ratio and mean square error are significantly improved. Finally, an industrial application of a shearer in a coal mining working face is performed to demonstrate the practical effect.

The traditional bistable stochastic resonance model has always had the drawback of being difficult when choosing accurate system parameters when a weak signal is enhanced. This paper proposes a parameter self-tuning adaptive optimization method based on the bat optimization algorithm to address this issue. The cubic mapping strategy of chaos optimization is introduced in the initial process of the individual position of the bat algorithm. Chaos is characterized by randomness, sensitivity, fractal dimension, and universality. The initial problem of the algorithm falling into local extremums is overcome. The global search capability of the basic bat optimization algorithm has been improved. The improved bat optimization algorithm’s objective function is the signal-to-noise ratio (SNR) of the target weak signal output by the bistable stochastic resonance model. An adaptive signal enhancement algorithm based on the improved bat optimization algorithm and bistable stochastic resonance (IBA-BSR) model is constructed to increase the proportion of weak signals in the mixed signal. Simulation signals are created to validate the proposed algorithm’s feasibility. The engineering application effect of this algorithm is further demonstrated by enhancing the sound signal of coal and rock cutting by a shearer in a coal face. Engineering test results demonstrate that this algorithm can significantly increase the SNR of coal and rock cutting sound signals by 42.4537 dB, and the effect is remarkable.

The vibration signal of the shearer is one of the important signals for coal and rock cutting mode recognition and fault diagnosis. However, the signal collected in the field contains a large amount of background noise, which is not conducive to subsequent analysis and processing. Therefore, a noise elimination method for coalcutter vibration signal based on Ensemble Empirical Mode Decomposition (EEMD) and an Improved Harris Hawks Optimization (HHO) algorithm is proposed in this paper. The vibration signal is first decomposed by EEMD to generate a series of intrinsic mode functions (IMF). The HHO algorithm was introduced to determine the optimal denoising threshold of each IMF. In addition, the original HHO has been improved to use the natural constant as the base exponential function to determine the escape energy trend line. Simulation results show that compared with the other four denoising methods, the signal waveform processed by this method is smoother. Under different types of signals and the same intensity of noise, the SNR increases by 70.9%, 6.7%, 2.6%, and 10.53% on average, respectively. The MSE decreases by 67.6%, 12.7%, 4.5%, and 5.42% on average. Under the same type of signal and different intensity of noise environment, the SNR is improved by 74.62%, 37.70%, 5.24%, and 39.72% on average, respectively. MSE decreased by 77.38%, 53.10%, 9.88%, and 54.67% on average. Finally, the method is applied to the shearer working state diagnosis system, and its actual effect is verified.