To plan a successful and practically executable path for a manipulator in a complex obstacle environment, the Improved_RRT method is proposed. In this article, we develop the collision detection model between the manipulator and obstacle based on the cylinder and sphere bounding box, and the shortest distance between the link and obstacle at each moment is obtained. Afterward, hybrid constrained sampling is used instead of original random sampling. The distribution of nodes obtained by sampling is close to the passages between obstacles. During the sampling process, the forward search process is accelerated. In the expansion, we propose a novel approach of RRT_Connect in collaboration with the artificial potential field method. Meanwhile, the adaptive gravitational field, dynamic step size, and new node constraint strategy are adopted. In the process of expansion, the algorithm uses the information of the environment and nodes to expand to the target area in a short time, which reduces the excessive exploration and collision area expansion. Considering the low success rate of algorithm planning in complex, multiobstacle and dynamic environments, local minimum processing and a dynamic path planning method are proposed, which generate double trees in the Cartesian and configuration spaces, respectively, for local path replanning. The improved algorithm is more robust to the singular problems of the manipulator, and the path is smoother after pruning optimization. Simulation results show that, compared with other algorithms, the proposed method can effectively plan a safe and optimal obstacle avoidance path with the best comprehensive performance. Experiments on the actual robotic manipulator platform further prove the efficiency of our method.

This communication presents an efficient iterative approach for locating the near-field (NF) coherent sources. In each iteration, the covariance matrices only containing single source information are constructed by using alternating oblique projection. Then based on the principle of vector dot products, a new iterative direction-of-arrival (DOA) estimator is proposed. After the DOA of each separated signal is estimated, the paired ranges are obtained from the 1-D maximum likelihood (ML) estimator. The proposed algorithm avoids high-dimensional spectral searches, subspace extraction, and any preprocessing such as spatial smoothing, leading to low computational complexities and high estimation accuracy. Comparative simulations show the efficiency and merits of the proposed method.

Automatic positioning technology of underground vehicles has become the key technology for the intelligent development of coal mines. To improve the comprehensive positioning accuracy of unmanned automatic vehicles in coal mines, an 18-D model of an odometer-aided inertial navigation system (INS) and a positioning model of an extended Kalman filter-based ultra-wideband (UWB) are established based on the vehicle kinematics equation. A tight integrated state estimation model is proposed to restrain the long-time drift of the INS and enhance the instability of the UWB system. A local positioning reference system based on an infrared motion capture system is established for the first time. Experimental tests show that the average positioning error of the proposed algorithm is 6.03 cm in the forward direction, and the root-mean-square error increased by 36.09% and 38.24% compared with those of the INS and UWB system, respectively. It is experimentally verified that the integrated positioning system achieves decimeter-level positioning accuracy in a coal mine roadway environment.

In this study, the tribological behavior of lamellar MoO₃ as a lubricant additive was investigated under different concentrations, particle sizes, normal loads, velocity, and temperature. The friction and wear tests were performed using a tribometer and with a reciprocating motion. The results indicate that the friction-reducing ability and antiwear property of the base oil can be improved effectively with the addition of lamellar MoO₃. The 0.5 wt % and 0.1 wt % concentrations of MoO₃ yield the best antifriction and antiwear effects, respectively. The maximum friction and wear reduction is 19.8% and 55.9%, compared with that of the base oil. It is also found the MoO₃ additive can decrease the friction considerably under a high velocity and normal load, and increase the working temperature. The smaller the size of MoO₃, the better the friction-reducing effect the lamellar MoO₃ shows. The friction-reducing and antiwear mechanisms of lamellar MoO₃ were discussed.