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Fast Hybrid PSO-APF Algorithm for Path Planning in Obstacle Rich Environment

机译：富障碍环境下路径规划的快速混合PSO-APF算法

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The present study aims to evolve a new hybrid algorithm for fast path planning in an obstacle-rich environment using the particle swarm optimization (PSO) and artificial potential field (APF). The proposed methodology combines the PSO with APF to improve the speed of determining a feasible and minimum cost path in an environment which has a high obstacle density. The proposed hybridization generates the way-points to be followed, by employing a combination of PSO and APF and it is found that in the environment with different obstacle densities, the hybrid algorithm arrives at the minimum cost feasible path in significantly lesser time in comparison to the basic PSO. It is also shown that the proposed hybrid algorithm is significantly faster than PSO even when there are moving obstacles and hence has the potential to be employed in real-time applications.
机译：本研究旨在使用粒子群优化（PSO）和人工势场（APF）来开发一种新的混合算法，用于在障碍较大的环境中进行快速路径规划。所提出的方法将PSO与APF相结合，以提高在具有高障碍物密度的环境中确定可行且成本最低的路径的速度。通过将PSO和APF结合使用，提出的混合生成了要遵循的航路点，并且发现在具有不同障碍物密度的环境中，与相比，混合算法以最小的时间到达了最小成本的可行路径。基本的PSO。还表明，即使在存在移动障碍物的情况下，所提出的混合算法也比PSO显着快，因此具有在实时应用中使用的潜力。

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