Many-objective evolutionary algorithm based agricultural mobile robot route planning

Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms.

Automated summary

Agricultural robot technology has developed rapidly, and route planning plays a crucial role in reducing navigation distance and saving total turning angle. This study uses a multi-objective evolutionary algorithm to solve the route planning problem for agricultural mobile robots, considering both path length and path angle. Four algorithms were compared, and HypE showed the best performance.