Study the path planning of intelligent robots and the application of blockchain technology

Abstract

With the improvement of China’s science and technology, robot technology has been widely developed and applied. The most significant feature of China’s intelligent robot technology advancement is the combination of embedded systems and robotics. Embedded systems have higher specific integration and lower power consumption. The binocular vision is introduced into the embedded system, and the inheritance and development of the algorithm is facilitated by the transplantation and use of the OpenCV vision library. Compared with the traditional method of using a separate vision chip, the development cost is saved and the application prospect is broad. Such advantages not only can fully meet the real-time nature of the system, but also can simplify the development of control software to a certain extent. The blockchain system using the proof-of-work mechanism can complete the issuance and transfer of digital assets in the presence of untrusted nodes. Different blockchains can obtain different security based on different economic incentives and punishment mechanisms. Among them, the proof of work is still the most secure consensus mechanism in the blockchain. According to the actual needs of embedded intelligent robot path planning, by comparing the advantages and disadvantages of existing algorithms and the practical ability of the external environment, a path planning algorithm based on the combination of A* and artificial potential field method is proposed. In the simulation experiments of this paper, the potential field ant colony algorithm rasterizes the environment, reduces the probability that the resultant force of the robot is zero, and increases the pheromone concentration in the direction of the resultant force, and converts the direction factor of the force into the pheromone factor. The ability to avoid obstacles is improved, the algorithm is prevented from falling into a local optimal situation, and the global optimal path is searched with path constraints (33.799).