Path planning of mobile robot based on particle swarm algorithm in complex situation

This paper presents a novel path planning method for mobile robots leveraging particle swarm optimization (PSO). The primary objective of this method is to efficiently generate smooth, obstacle-avoiding paths within a short timeframe, enabling the robot to navigate effectively from its starting point to the target point amidst complex environmental constraints. To achieve this goal, an enhanced PSO algorithm is proposed, incorporating an optimized Linearly Decreasing Inertia Weight (LDIW) strategy. This algorithm dynamically adjusts the inertia weight of individual particles based on their fitness values. This dynamic adjustment ensures a balanced exploration-exploitation trade-off during the search process, facilitating comprehensive exploration of the solution space while promoting rapid convergence to the optimal solution. Extensive simulation experiments demonstrate the effectiveness of the proposed approach, yielding highly promising results. The improved PSO algorithm, with its dynamic inertia weight adaptation, demonstrably enhances the efficiency of path planning for mobile robots navigating complex environments. The simulation results conclusively validate the algorithm’s capability in generating smooth obstacle avoidance paths.