Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms

Path planning is crucial for several applications, including those in industrial facilities, network traffic, computer games, and agriculture. Enabling automated path-planning methods in smart farms is essential to the future development of agricultural technology. Path planning is divided into global and local planners. Global planners are divided into different types and use well-known grid-based and sampling-based algorithms. In this paper, we propose an algorithm suitable for smart farms in combination with simultaneous localization and mapping (SLAM) technology. The characteristics of the grid-based Dijkstra algorithm, the grid-based A* algorithm, the sampling-based rapidly exploring random tree (RRT) algorithm, and the sampling-based RRT* algorithm are discussed, and an algorithm suitable for smart farms is investigated through field tests. We hypothesized path planning for an agricultural harvesting robot, a spraying robot, and an agricultural transport robot, and conducted experiments in environments with static and dynamic obstacles. In addition, the set parameters are validated experimentally. The Shapiro–Wilk test is used to confirm the shape of the normal distribution, and the analysis of variance (ANOVA) and Kruskal–Wallis test are performed to confirm the significance of the experimental results. Smart farms aim to minimize crop damage; thus, it is vital to reach the goal point accurately rather than quickly. Based on the results, we determined that the A* algorithm is suitable for smart farms. The results also open the possibility of reaching the correct destination in the shortest time when working in smart farms.