Path planning is one of the key factors determining the efficiency and quality of the first season harvest operation for regenerated rice. At present, there are many studies on the full coverage path planning technology of unmanned agricultural machinery in the operating area, but little consideration is given to the problem of the harvester crushing the regenerated rice during field harvesting. Therefore, research on the path planning of regenerated rice harvesting was conducted with reduced crushing. By analyzing farmland information, waiting areas, and unloading of grain, the problem of harvesting and unloading path planning for regenerated rice was transformed into a vehicle routing problem with capacity constraints (CVRP). A mathematical model for the harvesting path of regenerated rice was constructed with the goal of minimizing the rolling area and minimizing the total path of the harvester. A hybrid algorithm for path planning of regenerated rice with less compaction was proposed, using traditional ant colony algorithm (ACO) and 2-opt algorithm to obtain the optimal path. Taking the driverless harvester of ratooning rice as the object, the field experiments of straight path planning, headland turning, grain unloading planning and full link field operation were designed. The field experiments were conducted with the auto drive system to investigate the field compaction rate of the harvester. The results showed that the average absolute error of linear tracking was 3.51 cm, the maximum deviation was 8.24 cm, and the compaction rate of the linear segment was 17.55% . The compaction rate in the head area was decreased by 52.2% . The path planning designed had a total field compaction rate of 27.42% , which met the special operational requirements for regenerated rice. The research result can provide theoretical and technical support for reducing the crushing area of unmanned harvesting machines for regenerated rice.