Bistable piezoelectric energy harvester (BPEH) can remove mechanical energy waste, which is expected to realize the self-power supply of wireless sensors. To further improve the energy harvesting efficiency, we designed an improved bistable piezoelectric energy harvester (IBPEH). The restoring force model of the composing beam is acquired based on fitting experimental data, and the nonlinear magnetic model is obtained by using the magnetic dipole method. The electromechanical coupling dynamics model of the system is established based on Newton’s second law and Kirchhoff’s law. Based on the control variable method, the influences of excitation frequency and excitation amplitude on the vibration characteristics of IBPEH and BPEH are compared in simulation analysis. Moreover, the correctness of the theoretical analyses is verified by experiments. The results show that variations in the number of magnets and appropriate adjustments in their positions can broaden the operating frequency bandwidth of the bistable piezoelectric energy harvester, and realize large-amplitude periodic motion at lower excitation amplitudes. IBPEH can yield a higher voltage than BPEH under the same excitation conditions. This paper provides a theoretical basis for optimizing the potential well and further improving the electric energy harvest efficiency of the bistable piezoelectric energy harvester device.

Permanent highway support in deep coal mines now depends on the anchor drilling robot’s drill arm. The drilling arm’s trajectory planning using the conventional RRT (rapid-expanding random tree) algorithm is inefficient and has crooked, rough paths. To improve the accuracy of path planning, we propose an improved RRT algorithm. Firstly, the kinematic model of the drill arm of the drill and anchor robot was established, and the improved DH solution parameters and the positive solution of the drill arm kinematics were solved. The end effector’s attainable working space was calculated using the Monte Carlo approach. Additionally, to address the problem of the slow running speed of the RRT algorithm, an artificial potential field factor was introduced to construct virtual force fields at obstacle and target points and calculate the potential field map for the entire reachable workspace to improve the speed of the sampling points close to the target point. At the same time, the greedy approach and the three-time B-sample curve-fitting method were used simultaneously to remove unnecessary points and carry out smooth path processing in order to improve the quality of the drill arm trajectory. This was carried out in order to solve the issue of rough pathways generated by the RRT algorithm. Finally, 50 time-sampling comparison experiments were conducted on 2D and 3D maps. The experimental results showed that the improved RRT algorithm improved the average sampling speed by 20% and reduced the average path length by 14% compared with the RRT algorithm, which verified the feasibility and effectiveness of this improved RRT algorithm. The improved RRT algorithm generates more efficient and smoother paths, which can improve the intelligence of the support process by integrating and automating drilling and anchoring and providing reliable support for coal mine intelligence.