Objective  To solve the problems that picking robot could not find the multipath quickly and accurately in planning route in complex plantation environment, a genetic algorithm based on group and elite strategy (GGABE) was proposed.

Method  Firstly, an initial population was generated and was divided into several groups using the Sigmoid function. After n times of operations of selections, crossovers and mutations in each group separately, k optimal paths with equal length were then acquired in each group. Comparing the optimal paths among different groups, the shortest paths were chosen as the final optimal paths. With all population parameters being the same, three types of algorithms, including simple genetic algorithm(SGA), ungrouped elite genetic algorithm (EGA) and GGABE, were tested 50 times respectively on 15×15 and 25×25 maps. The prototype verification experiments were carried out in the plantation.

Result  Eight shortest paths with the average length of 20.970 6 were found in map 1 by GGABE. Only one shortest path was found in map 1 with the other two algorithms. Eight shortest paths with the average length of 38.041 6 were found in map 2 by GGABE. Three optimal paths were found in each of the 50 verification experiments, and the average consumption time for route planning was 15.543 319 s.

Conclusion  GGABE has fast convergence speed and can quickly and accurately find out all optimal paths, which are able to traverse the entire plantation, from the map.