Dynamic anti-collision A-star algorithm for multi-ship encounter situations

He, Zhibo; Liu, Chenguang; Chu, Xiumin; Negenborn, Rudy R.; Wu, Qing

doi:10.1016/j.apor.2021.102995

Cited by 92 publications

(29 citation statements)

References 39 publications

(41 reference statements)

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“…From Figure 10, it can be assumed that the positions of enemy NPCs and players are represented by [X, Y]. The position of the enemy NPC is at [0, 3], and the position of the player is at (5,7) . Then several calculations will be carried out to determine https://www.indjst.org/ which paths the enemy NPC should take in accordance with the A-star algorithm calculations that have been implemented.…”

Section: Resultsmentioning

confidence: 99%

“…-Player (5,7) H(x) = 9 2. Calculate F(x) from nodes around [0, 3] towards the position of the player.…”

Section: Resultsmentioning

confidence: 99%

“…Calculate F(x) from nodes around (5,5) towards the position of the player -Node (5,5) -Node (5,6) F(x) = 78 (Node selected) 8. Calculate F(x) from nodes around (5,6) towards the position of the player -Node (5,6) -Node (5,7) F(x) = 78 (Node selected) https://www.indjst.org/ 9. Position of player found.…”

Section: Resultsmentioning

confidence: 99%

“…There is only one destination for enemy NPC in this research, which is the player's character. The A-star algorithm has a fast computation time, and this algorithm works by determining in advance two points such as points A and B, where point A is the origin point while point B is the destination point (6,7) . Usually, between points A and B, there is a barrier that will determine the number of turns and the shortest or longest path from A to B. https://www.indjst.org/ In the pathfinding process, the A-Star algorithm has one weakness, it needs to check every single existing node and calculate the heuristic value.…”

Section: Introductionmentioning

confidence: 99%

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A-star Optimization with Heap-sort Algorithm on NPC Character

Chandra¹,

Istiono²

2022

IJST

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Objectives: To find out whether the A-star algorithm that is optimized with the heap-sort algorithm can reach the target destination faster than the native Astar when applied on NPC to find the path. Methods: Comparisons are made by implementing an optimized A-star algorithm with heap-sort and native Astar on an NPC in a dungeon-crawling game genre by applying five different scenarios, where each scenario has a different start position of the NPC, and the target goal is set. The comparison is made by measuring the time required by both algorithms to reach the target goal in milliseconds with Unity engine software and C# language. Findings: From the results of experiments and calculation, it was found that the A-star algorithm optimized with the heap-sort algorithm was 50%-80% faster than using only the A-star algorithm in cases that was applied alone on NPC in dungeon-crawling game. Novelty: The novelty in this research is the optimization of the A-star algorithm with heap-sort, and then that algorithm is compared with the native A-star algorithm that is applied to NPCs in the dungeon-crawling game genre.

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Section: Resultsmentioning

confidence: 99%

“…-Player (5,7) H(x) = 9 2. Calculate F(x) from nodes around [0, 3] towards the position of the player.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A-star Optimization with Heap-sort Algorithm on NPC Character

Chandra¹,

Istiono²

2022

IJST

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“…However, the limitations of traditional pathfinding are highlighted by the single pathfinding result, low intelligence, and large gap with manual selection. The traditional A* algorithm can only avoid static obstacles, while a dynamic anti-collision A* algorithm is proposed to solve the path planning problem with dynamic obstacles in multiship encounter scenarios [33]. The simulation results show that the dynamic A* algorithm can generate more reasonable dynamic and static obstacle avoidance paths in complex traveling scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning

Wang

Xie

Liu

et al. 2022

IJERPH

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Public safety and health cannot be secured without the comprehensive recognition of characteristics and reliable emergency response schemes under the disaster chain. Distinct from emergency resource allocation that focuses primarily on a single disaster, dynamic response, periodic supply, and assisted decision-making are necessary. Therefore, we propose a multiobjective emergency resource allocation model considering uncertainty under the natural disaster chain. Resource allocation was creatively combined with path planning through the proposed multiobjective cellular genetic algorithm (MOCGA) and the improved A* algorithm with avoidance of unexpected road elements. Furthermore, timeliness, efficiency, and fairness in actual rescue were optimized by MOCGA. The visualization of emergency trips and intelligent avoidance of risk areas were achieved by the improved A* algorithm. The effects of logistics performance, coupling of disaster factors, and government regulation on emergency resource allocation were discussed based on different disaster chain scenarios. The results show that disruption in infrastructure support, cascading effect of disasters, and time urgency are additional environmental challenges. The proposed model and algorithm work in obtaining the optimal solution for potential regional coordination and resilient supply, with a 22.2% increase in the total supply rate. Cooperative allocation complemented by political regulation can be a positive action for successfully responding to disaster chains.

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