A Hybrid Optimization Method for Path Planning and Obstacle Avoidance in Cluttered Environments

Abstract

Hybrid optimization methods are a promising approach for solving complex optimization problems, and they have gained popularity in recent years due to their ability to effectively combine the strengths of multiple algorithms. In this article, we propose a hybrid optimization method for finding the optimal path for a wheeled ground robot to navigate through a cluttered environment while avoiding obstacles. Our approach combines an optimization algorithm, which is used to generate a diverse set of initial solutions, with an evolutionary algorithm, which is used to optimize these solutions. The optimization algorithm is able to perform a global search of the solution space, while the evolutionary algorithm is able to quickly converge on high-quality solutions. By combining these two algorithms, we are able to take advantage of the strengths of both approaches and find the optimal path in a relatively efficient manner. We evaluate the performance of our hybrid optimization method through simulation experiments on a variety of path planning and obstacle avoidance tasks. The results show that our approach is able to find the optimal path in a timely manner and outperforms other state-of-the-art methods. In summary, our proposed hybrid optimization method is a promising approach for finding the optimal path for a wheeled ground robot to navigate through a cluttered environment while avoiding obstacles. By combining an optimization algorithm and an evolutionary algorithm, we are able to effectively explore a wide range of solutions and find high-quality solutions in a relatively efficient manner.