A Safe Heuristic Path-Planning Method Based on a Search Strategy.

Sensors (Basel)

School of Information Science and Engineering, Harbin Institute of Technology at WeiHai, No. 2 Wenhua West Road, Weihai 264209, China.

Published: December 2023

AI Article Synopsis

  • The paper proposes a heuristic path-planning method for robots in industrial settings, focusing on safe and efficient navigation by evaluating node states and danger coefficients.
  • This method outperforms the traditional A-star algorithm by reducing path length by 2.89%, execution time by 13.98%, and significantly improving path smoothness by 93.17%.
  • The improved path planning not only enhances safety and reliability for robots but also minimizes power consumption, helping to preserve battery life during operations.

Article Abstract

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10780702PMC
http://dx.doi.org/10.3390/s24010101DOI Listing

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