Robot path planning in a dynamic environment with stochastic measurements

Obstacle avoidance operations of tractors can cause parts of land to be unavailable for planting crops, which represents a reduction in land utilization. However, land utilization is significant to the increase in agricultural productivity. Traditional obstacle avoidance path planning methods mostly focus on automatic tractor navigation with small errors, ignoring the decrease in land utilization due to obstacle avoidance operations. To address the problem, this paper proposed an obstacle avoidance path planning method based on the Genetic Algorithm (GA) and Bezier curve. In this paper, a third-order Bezier curve was used to plot the obstacle avoidance path, and the range of control points for the third-order Bezier curve was determined according to the global path and the location of the obstacle. To target the navigation error and land utilization problems, GA was used to search for the optimal point from the selection range of the control point under multiple constraints for automatic tractor navigation such as the obstacle collision avoidance, the minimum turning radius, and the maximum turning angle. Finally, the optimal obstacle avoidance path was determined based on the selected control points to minimize the navigation error and maximize land utilization. The algorithm proposed in this paper was compared with existing methods and the results showed that it has generally favorable performance on obstacle avoidance path planning.

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Robot path planning in a dynamic environment with stochastic measurements

Cited by 3 publications

References 19 publications

Fastest motion planning for an unmanned vehicle in the presence of accelerating obstacles

Fastest motion planning for an unmanned vehicle in the presence of accelerating obstacles

Path planning algorithms in the autonomous driving system: A comprehensive review

Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste

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