Abstract: According to the rules for the 4th national contest of Smart car, starting from the black line, the smart car should stop within three meters automatically after one routines racing. If it fails, the racing marks have to add one second. During the contest, one second is of utmost importance for the final marks. Therefore, an accurate starting line identification algorism is necessary. Three starting line identification algorisms were discussed in this paper. Based on the experiments results, one optimal algorism was given, which has been proved with the racing for identifying the starting line.
Abstract: According to the rules for the 4th national contest of Smart car, starting from the black line, the smart car should stop within three meters automatically after one routines racing. If it fails, the racing marks have to add one second. During the contest, one second is of utmost importance for the final marks. Therefore, an accurate starting line identification algorism is necessary. Three starting line identification algorisms were discussed in this paper. Based on the experiments results, one optimal algorism was given, which has been proved with the racing for identifying the starting line.
Abstract: According to the rules for the 4th national contest of Smart car, starting from the black line, the smart car should stop within three meters automatically after one routines racing. If it fails, the racing marks have to add one second. During the contest, one second is of utmost importance for the final marks. Therefore, an accurate starting line identification algorism is necessary. Three starting line identification algorisms were discussed in this paper. Based on the experiments results, one optimal algorism was given, which has been proved with the racing for identifying the starting line.