Abstract
This study is a part of the trajectory planning applied to harvest system work where mobile robots must be able to navigate safely the environment to look for palmer crops. Many constraints can be faced, such as crop selection as maturity changes over time, searching for the most mature palmer, avoiding different kinds of obstacles, robot speed control, and the cost of moving from an initial point to a goal target. After studying different trajectory planning approaches and their applications [8], we conclude that some of these methods can be combined to design a new, powerful approach based on the accurate property of Dijkstra and the heuristic function of A Star.Dijkstra is known as a powerful algorithm based on graph mapping and reducing the path cost, and A Star on the other side is one of the best guides for path searching due to the heuristic function that avoids exploring all environment nodes and only those leading to the goal. Combining Dijkstra’s algorithm and the A* (A-star) algorithm can lead to a more efficient pathfinding approach. Dijkstra’s algorithm [4] is a well-known method for finding the shortest path between two nodes in a graph, while the A* algorithm is an extension of Dijkstra’s algorithm that uses heuristic estimates to guide the search towards the goal node. By combining these two algorithms, we can use Dijkstra’s algorithm to explore the graph and generate a good initial estimate of the path cost, then use the A* algorithm to refine the estimate and guide the search towards the goal node. This paper explores the utilization of trajectory planning in a harvesting system. By employing both the Dijkstra and A* algorithms, we propose a hybrid approach to ensure optimal timing for finding a path. We conduct a comparative analysis to evaluate the performance of the new approach by comparing the application of a single algorithm versus the hybrid approach across various graph sizes.
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Ait Ben Mouh, L., Ouhda, M., El Mourabit, Y., Baslam, M. (2023). A Hybrid Approach of Dijkstra’s Algorithm and A* Search, with an Optional Adaptive Threshold Heuristic. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_9
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