Adaptive turning control for an agricultural robot tractor

Hao Wang, Noboru Noguchi

Abstract


An adaptive turning algorithm for a four-wheel robot tractor in the headland is presented in this paper. The navigation sensors consisted of an inertial measurement unit and a real-time kinematic global positioning system (GPS). An objective function based on weights was used to create the navigation path, connecting by continuous primitives. The asymmetric steering mechanism was then taken into consideration with a vehicle model. To follow the path accurately, the slide movement of the robot and the steering rate were taken into account by estimating the turning radius in real time. In addition, the vehicle model was tuned based on the results of each turn. Therefore, the turning control algorithm was optimized on the basis of the specific conditions in the field. Field experiments showed that the robot tractor approached the next path with an average lateral deviation of 3.9 cm at a speed of 1.2 m/s during a turn. Compared to a conventional turning scheme, the time consumption and turning trajectory were decreased by 17% and 21%, respectively.
Keywords: autonomous tractor, path planning, dynamic circle-back turning, switch-back turning, robot tractor, reinforcement learning
DOI: 10.25165/j.ijabe.20181106.3605

Citation: Wang H, Noguchi N. Adaptive turning control for an agricultural robot tractor. Int J Agric & Biol Eng, 2018; 11(6): 113–119.

Keywords


autonomous tractor, path planning, dynamic circle-back turning, switch-back turning, robot tractor, reinforcement learning

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References


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