Effectiveness of Robot Motion Block on A-Star Algorithm for Robotic Path Planning

Kabir, Raihan; Watanobe, Yutaka; Naruse, Keitaro; Islam, Rashedul

doi:10.3233/faia220241

Cited by 2 publications

(1 citation statement)

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“…The RMB is a matrix that specifies the cells that the robot should search in each iteration to find the goal node, starting from the start node. The performance and efficiency of the algorithm are significantly affected by the size of the motion block matrix, as it determines the search area of the robot in each iteration [ 12 ]. An inefficient motion block results in the robot searching for a larger number of cells to find the goal, increasing the time and space complexity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning

Kabir,

Watanobe,

Islam

et al. 2024

Sensors

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An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

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Section: Introductionmentioning

confidence: 99%