An effective mobile robot path planner by optimizing multiple performance parameters inspired by sparrow’s Anti-predation exploration ability
摘要
Mobile robot path planning is a significant area of research that involves determining the most effective path for a robot to reach its destination by avoiding obstacles. The most crucial aspect of mobile robot path planning is selecting the appropriate algorithm. The existing optimization algorithms used different strategies but still faced some issues, such as longer path length, slow convergence rate, less smoothness, and frequent oscillation. Therefore, considering the challenges faced by existing algorithms, a novel smart sparrow search algorithm (SSSA) is proposed. The proposed technique takes advantage of the sparrow’s anti-predation exploration ability for mobile robot path planning. Three different strategies are proposed in the smart sparrow search algorithm. Firstly, a smooth path search (SPS) approach based on the Steiner waypoint is adopted to enhance the attained path smoothness. Secondly, a neighborhood search approach based on predator (obstacles) distance is employed to improve the predation rate of the population. Lastly, a new location update approach based on a robust learning factor is applied to increase the convergence rate. Several comparative experiments are carried out in three different test situations of varying complexity. Different multiple path planning performance parameters such as path length, convergence rate, path smoothness, and execution time are being assessed to prove the efficacy of the proposed technique. The comparative analysis signifies that the proposed technique surpasses the improved ACO, ACO-