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An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. The 3D simultaneous localization and mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on normally distributed transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to building a globally consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.

Overwinding protection devices are used to brake hoisting containers before these containers reach a limited height, thereby preventing the hoisting containers from impacting the hoisting system. However, in ultra-deep shafts (depth > 1000 m), traditional overwinding protection methods fail to protect the hoisting system, because this type of hoisting system has a greater mass, kinetic energy, and inertia than the traditional hoisting system, and also the environment of ultra-deep shafts is more complex. This paper presents a novel overwinding protection method that applies a linear permanent magnet eddy current brake (LPMECB) to the hoisting system in ultra-deep shafts. This paper also finds the optimum setting parameter of permanent magnets (PMs). First, an analytical model of the LPMECB is built, and the time-domain signals of the braking force are processed via fast Fourier transform, confirming the mechanism of the optimum setting parameter. Subsequently, the simulations are conducted by establishing a finite-element model of the LPMECB; the simulations prove the existence of the optimum setting parameter of PMs and demonstrate the influence of the air gap, velocity, and conductivity on this parameter. Finally, the experimental studies are carried out on a test bench of the LPMECB to validate the analytical model and the simulation results. The results show the existence of the optimum setting parameter of PMs and prove that the air gap has an effect on this parameter.

An adaptive dynamic surface controller (ADSC), which comprises an online parameter estimator and a robust control law, is developed for the pneumatic servo systems driven by the proportional directional control valves. Departing from the use of time-consuming offline fitting of the orifice area to accommodate the effect of valve dead zone, this paper employs the least-square-type indirect parameter estimation algorithm with online condition monitoring to estimate the dead-zone parameters and some other important model parameters. These accurate estimates of model parameters are utilized in the development of a precise position tracking controller for the single-rod pneumatic actuator. By using the dynamic surface control technique to synthesize the robust control law, the problem of “explosion of complexity” in traditional backstepping design method is avoided. The stability of the closed-loop system is proved by the means of the Lyapunov theory. The obtained extensive comparative experimental results verify the effectiveness of the proposed valve dead-zone compensation strategy and the high-performance nature of the ADSC in practical implementation.