Global Path Planning Method by Fusion of A-star Algorithm and Sparrow Search Algorithm

Chen, Yangde; Lin, Zichen; Sun, Chenhao

doi:10.1109/ddcls55054.2022.9858435

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…We achieve this by arranging feature hash values in ascending order of rank. Additionally, to address data management complexity, we employ specialized KD trees [38] for each product type. These trees, indexed by product hash sets, efficiently handle all possible values of any feature, making it possible to index a large number of products of the same type with just a single KD tree.…”

Section: Figure-4 Indexing and Hashing Model Of Global Datasetmentioning

confidence: 99%

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

B. Tarakeswara Rao

2024

jes

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Dynamic Product Recommender systems play a crucial role in e-commerce, guiding users towards products that align with their preferences and enhancing the shopping experience. However, existing systems often struggle to capture the nuances of user preferences, particularly when dealing with multiple features via keyword queries and filters. Additionally, scalability challenges arise as the volume of products grows with features and values. In addition, the static filters of existing recommender systems limit the exploration by strictly adhering to user selections, hindering discovery of potentially better features within the preferred budget or category. This can lead to user dissatisfaction and missed opportunities to discover more desirable options. This research addresses these limitations by introducing FacetDRS, a novel dynamic product recommendation system that leverages flexible feature matching and a scalable architecture. FacetDRS empowers the recommendations with three distinct matching methods to cater to diverse preference nuances. Exact Match ensures precise adherence to user specifications, while High-Impact Partial Match prioritizes core features with flexibility in non-critical aspects. Extended Partial Match suggests relevant options beyond initial preferences, leveraging predefined thresholds. This multi-faceted approach captures the full spectrum of user desires while maintaining accuracy. To evaluate the effectiveness of FacetDRS, a rigorous experimental setup was conducted, comparing its performance across Keyword Match Score, First Selections in Top-K and Average Response Time metrics with established recommender systems such as DeepRec, APGNN, and TADCF. The results demonstrate that FacetDRS achieves superior performance than its counterparts in all comparison aspects. The system's flexible matching methods, combined with its robust architecture, offer a valuable solution for enhancing user satisfaction and driving conversions in the mobile commerce landscape.

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Section: Figure-4 Indexing and Hashing Model Of Global Datasetmentioning

confidence: 99%

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

B. Tarakeswara Rao

2024

jes

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“…Wu et al [75] proposed a hybrid dynamic path planning algorithm for forklift AGV by improving A* with Dynamic Window Algorithm (DWA). Chen et al [76] proposed a shortest path planning problem in AGV static environment by integrating an A* and Sparrow Search Algorithm (SSA). Bai et al [77] proposed an improved A* algorithm-based motion planning and tracking control strategy based on model predictive control in order to solve problem and tracking control for autonomous vehicle.…”

Section: ) A-starmentioning

confidence: 99%

“…The method also handles unexpected obstacles in the path robustly Chen et al [72] Improved A* To propose two-stage congestionminimizing routing method Success to increase efficiency and bringing huge economic benefits to the warehouses Tang et al [73] Improved A* To avoid the problems of several nodes, long distance and large turning angle. Traditional A* algorithm limitation usually exist in the sawtooth and cross paths Success to reduces the number of nodes by 10% 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5% Chen et al [74] A* To find shortest path between two points in path planning Success to identified that A* is better and shorter than ACO Wu et al [75] Improved A* To plan FAGV the global optimal path and more suitable Success to improve A* algorithm in simulation the number of paths turns of the is reduced by 62.5%, the smoothness is higher, and the turning angle is smaller Chen et al [76] A* To find AGV shortest path planning in a static raster environment problem Success to demonstrates the effectiveness of the method and provide some reference for the shortest path planning of AGV Bai et al [77] Improved…”

Section: Zhao Et Al [25]mentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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“…The experimental environment was run on a CPU with 8GB memory and a 64-bit WIN10 system, and two different 2D raster map environments were designed for simulation. In the 21×21 simple environment map 1, the starting point S (5,16) and the ending point T (19,3); in the 21×21 maze environment map 2, the starting point S (2,20) and the ending point T (19,3). The coefficients involved in the improved DWA algorithm are =0.2, =0.1, =0.3, and =0.4.…”

Section: Simulation Experiments 51 Simulation Experimental Environmentmentioning

confidence: 99%

“…Based on local path planning, greedy algorithm, A* algorithm, Dijkstra algorithm, D* algorithm (a variant of Dijkstra algorithm), etc. [1][2][3][4][5]. Based on global planning.…”

Section: Introductionmentioning

confidence: 99%

Robot Path Planning Research Incorporating Improved A* Algorithm and DWA Algorithm

Qu,

Feng,

Jiang

et al. 2023

ITC

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For the traditional A* algorithm has problems such as long paths, large number of nodes, and the demand for dynamic obstacle cannot be avoided in complex environment. A mobile robot dynamic path avoidance method will be improved to improve the A * algorithm and improve DWA algorithm Two map environments are used for simulation verification. First, the evaluation function and key node selection strategy are optimized for the A* algorithm, and redundant nodes are deleted; then the dynamic obstacle distance evaluation function is added to the DWA algorithm which for the purpose of the obstacle avoidance performance can be enhanced. The results about the improved A* algorithm reduces 12.20% and 58.33% in path length and number of turning points respectively compared with the traditional A* algorithm can be obviously grasped by the simulation experiment; by using the fusion algorithm whose purpose of using arcs instead of the straight lines is to turn more smoothly, and can be closest to the global optimum while avoiding dynamic obstacles to complete the search.

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Global Path Planning Method by Fusion of A-star Algorithm and Sparrow Search Algorithm

Cited by 13 publications

References 6 publications

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

Robot Path Planning Research Incorporating Improved A* Algorithm and DWA Algorithm

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