Motion Planning for Autonomous Grain Carts

Abstract

When harvesting grain crops on large farms, a combine collects the grain while a grain cart transports the grain by commuting between the combine and a semi-trailer parked by the roadside. There are several issues associated with human-operated grain carts: labor shortage and increasing labor cost, operational imprecision and inefficiency as well as safety hazards, all of which can potentially be addressed if grain carts were autonomous. This paper presents a motion planning algorithm and the associated navigation solution for autonomous grain carts. The algorithm features a novel integration of Artificial Potential Field (APF) with Fuzzy Logic Control (FLC). A set of simulation tests were carried out, comparing the proposed APF+FLC planner with a simple APF planner. The test results verified the effectiveness, robustness, and efficiency of the proposed planning algorithm in performing the logistical tasks in harvest operations where unharvested crops were the only obstacles as well as when random static or dynamic obstacles existed. In addition, a set of mobile robot tests implementing the proposed navigation solution were conducted, in which the robot representing the grain cart autonomously accomplished the logistical tasks in the harvest operations, verifying the effectiveness and practicality of the navigation solution.