Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Abstract

With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

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“…Under normal circumstances, the path planned by the blue vehicle cannot pass through these areas under the influence of the structured road. At this time, it can consider Yi et al's improved P_RRT * algorithm [22], Xiang et al's improved A * algorithm [23] or Wang et al's PGI-RRT * algorithm [24]. These algorithms are effective algorithms in the face of complex static obstacles.…”

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“…In self-pollination, a Cauchy mechanism is used to decide which neighboring structure to choose for updating the population, is shown in Eq. (8). Finally, the tabu list mechanism is integrated for further strengthen the local exploration capabilities of the algorithm.…”