An obstacle avoidance path planner for an autonomous tractor using the minimum snap algorithm

Xin Zhao

doi:10.1016/j.compag.2023.107738

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An obstacle avoidance path planner for an autonomous tractor using the minimum snap algorithm

2023-04-01

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DOI：10.1016/j.compag.2023.107738

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Abstract

En 中文

Autonomous tractors use a GNSS-based approach combined with other sensors to provide higher efficiency and minimize human intervention. The basis of field automated navigation is the agricultural route planning of the entire field. One of the main aspects of agricultural route planning is obstacle avoidance path planning, which can provide a safe continuous trajectory for autonomous tractors to pass obstacles. In this study, a collision-free path planning method was proposed using the minimum snap algorithm to enable the tractor to safely navigate from one point to another. To authenticate the proposed algorithm, a tripartite experimental scheme was designed, encompassing a ROS-based simulation, robot platform validation, and a field test. Simulation results demonstrate that the collision-free path generated by the proposed algorithms can effectively guide the tractor to follow the given path with a lateral error of 1.75 cm. For the robot platform validation test, the mean lateral deviations were 2.63 cm, 5.01 cm, and 6.80 cm at speeds of 0.5 m/s, 0.8 m/s, and 1.0 m/s, respectively. The feasibility of using the developed algorithms was followed by a field test with a 162-KW CVT autonomous tractor. The mean lateral deviations were 5.17 cm, 5.13 cm, and 6.10 cm at speeds of 0.6 m/s, 0.8 m/s, and 1.0 m/s, respectively. The test results showed that the model could provide a safe and stable collision-free path for agricultural machines operating in the field.

Keywords:

Minimum snap algorithm

Autonomous tractor

Obstacle avoidance path planning

Optimization model

Journal

Computers and Electronics in Agriculture

IF:

8.9

Papers:

1.0W

Citations:

4.8W

Organization

china agricultural university

Scholars:

5.1W

Papers: 3.0W

Citations: 43

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