Abstract
Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.
Highlights
In recent years, automotive engineering has been an emerging area
Parallel strategy refers to strengthening the communication among groups and reducing the defects of the original arithmetic optimization algorithm (AOA), such as premature convergence, search stagnation, and easy to fall into the local optimal search space. e main contributions of this article are summarized as follows: (1) We propose a novel parameter adaptive equation to control the AOA sensitive parameter α, which can balance the capabilities of exploration and exploitation
Another coefficient defined by equation (2) is Math Optimizer Probability (MOP), which is employed for controlling the range of candidate solutions in the phase of exploring or exploiting
Summary
Automotive engineering has been an emerging area. Among it, the automotive robots have been widely employed in industry and social life and played an important role, especially during the pandemic of COVID19. e existing literatures have explored issues on robot path planning. Zhang et al [21] proposed a new improved artificial fish swarm algorithm (IAFSA) to process the mobile robot path planning problem in a real environment. Parallel strategy refers to strengthening the communication among groups and reducing the defects of the original AOA, such as premature convergence, search stagnation, and easy to fall into the local optimal search space. (1) We propose a novel parameter adaptive equation to control the AOA sensitive parameter α, which can balance the capabilities of exploration and exploitation (2) We propose a novel parallel communication strategy and apply it to AOA, which can strengthen the communication and information exchange among groups and avoid falling into the local optimal solution (3) e improved AOA is applied to an optimization problem of 2D robot path planning e rest of the article is organized as follows.
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