Dynamic Path Planning Method for Flat-Land Fancy Roller Skating Based on Multi-Sensor Information Fusion

With the improvement of sports enthusiasts' competitive level, the path planning of flat-land fancy roller skating has become a concern for people. This research mainly discusses the method of dynamic path planning for flat fancy roller skating based on multi-sensor information fusion. This research first uses the particle swarm algorithm to search for the global optimal path, and then uses the ant colony algorithm to perform a second search on the optimal path obtained by the particle swarm algorithm to obtain the optimal solution. Applying voltage stimulates the transducer to emit ultrasonic waves in pulse form, after which it transitions into a receiving state to analyze the received ultrasonic waves. Once the inspection confirms that the signal it detected is the echo of the emitted ultrasonic wave, it calculates the distance between the measured obstacle and the sensor using the transit time principle. The mobile pulley can measure the coordinate information of the mobile pulley in the global environment through the positioning measuring instrument and then compare it with the previously planned path to determine the position deviation of the mobile pulley. In order to improve the real-time performance and reliability of the system, the data collection and data fusion of the sensors are realized by a two-level distributed system. The ultrasonic sensor and its control interface board adopt the single-chip microcomputer control system produced by the American HelpMate Company. The ultrasonic transducer is both a transmitter and a receiver. The maximum measuring distance of the ultrasonic sensor can be programmed to be 0.1m-10m. The robust performance index (ER) is very different before and after the improvement. The improved algorithm is only 0.87%, which is far less than 8.12% in the basic ant colony algorithm. In the case of the same number of iterations, the path length of the improved algorithm is longer than that of the basic algorithm. This research will greatly enhance the superiority of dynamic path planning for flat-land fancy roller skating.