Motion Planning for Autonomous Grain Carts Academic Article uri icon


  • In crop harvesting, a combine travels around in the field to collect grain while a grain cart commutes between the combine and a semi-trailer by the roadside to transport the grain. There are several problems associated with human-operated grain carts: labor shortage and increasing labor cost, operational imprecision and inefficiency as well as safety hazards. All of these problems can potentially be addressed if grain carts were autonomous. To facilitate full autonomy of grain carts, this study develops a motion planning algorithm, featuring a novel integration of Artificial Potential Field (APF) with Fuzzy Logic Control (FLC). In addition, this study proposes a high-level software and hardware solution to building the navigation systems for implementing the developed motion planning algorithm on autonomous grain carts, covering sensor selection, communication options, control technique and actuation plan. A set of simulation tests featuring the comparison between the proposed APF+FLC planner and a simple APF planner were carried out in MatLab Simulink. The simulation tests demonstrated that the proposed motion planning algorithm and the associated task scheduling strategy could promptly direct an autonomous grain cart to intelligently perform the logistical tasks in harvesting operations where unharvested crops were the only obstacles as well as when random static or dynamic obstacles existed, outperforming the simple APF planner in trajectory length and smoothness by roughly 15% to 20%. In addition, another set of simulation tests comparing the proposed APF+FLC planner with a Vector- Field-Histogram (VFH) planner were conducted to further evaluate the performance of the proposed algorithm. It was shown that although the VFH planner tended to plan smoother paths, the APF+FLC planner was superior in terms of generating shorter paths with less computational cost (shorter and less both by as much as 60%). Results of the two sets of simulation tests verified the effectiveness, robustness, efficiency and computational ease of the proposed motion planning algorithm. Following the simulation tests, a set of mobile robot tests implementing the proposed navigation solution were conducted, in which the proposed algorithm was effective in directing the grain cart to intelligently accomplish the logistical tasks in harvest operations. Additionally, the mobile robot tests included a variety of more general obstacle avoidance cases, in which the proposed algorithm was always effective in leading the robot to efficiently accomplish the navigation tasks, outperforming a simple APF planner in trajectory length by as much as 25% and in smoothness by as much as three times. The mobile robot tests verified the effectiveness and practicality of the proposed navigation solution as well as the effectiveness, robustness, and especially efficiency of the proposed motion planning algorithm.

published proceedings


author list (cited authors)

  • Shangguan, L., Thomasson, J. A., & Gopalswamy, S.

citation count

  • 5

complete list of authors

  • Shangguan, Lantian||Thomasson, J Alex||Gopalswamy, Swaminathan

publication date

  • March 2021