In order to solve the problem of path planning of mobile robots in a dynamic environment, an improved rapidly-exploring random tree^* (RRT^*) algorithm is proposed in this paper. First, the target bias sampling is introduced to reduce the randomness of the RRT^* algorithm, and then the initial path planning is carried out in a static environment. Secondly, apply the path in a dynamic environment, and use the initially planned path as the path cache. When a new obstacle appears in the path, the invalid path is clipped and the path is replanned. At this time, there is a certain probability to select the point in the path cache as the new node, so that the new path maintains the trend of the original path to a greater extent. Finally, MATLABis used to carry out simulation experiments for the initial planning and replanning algorithms, respectively. More specifically, compared with the original RRT^* algorithm, the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19% on average.

In order to improve the heating efficiency of the electric heating system for heavy oil wells and solve the current situation that the heating operation control of the medium frequency power supply in the oilfield mainly relies on manual experience settings and low-efficiency automatic control resulting in electric power waste, an embedded control device for heavy oil electric heating was designed. Based on the non-embedded oil well production electrical signal, the electric power of the heavy oil production motor was used as the closed-loop feedback signal to obtain the reference input of the optimal oil temperature. On this basis, through the differential feedback link of the wellhead oil temperature, the temperature hysteresis effect was improved, and the energy-saving optimization control of the medium frequency power supply was realized. Through the design of the embedded hardware core circuit module and the development of the main software functions, not only the dynamic control of the electric heating process of heavy oil wells was realized, but also realized the real-time monitoring of various operating indicators on-site and in the cloud. The heavy oil well field test analysis showed that the designed heavy oil electric heating embedded control device could meet the production requirements of energy saving and safety in the well field, and the energy consumption was saved by 20%.