L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

Niewola, Adam; Podsedkowski, Leszek

doi:10.1007/s10846-017-0748-6

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…Inside the “while” loop, D* shortest path algorithm must be performed for every emergency exit. The time complexity of D* shortest path algorithm is

which is the same as the time complexity of the A* algorithm [ 38 , 39 ]. Then the time complexity for the DBFS algorithm is

, where

is the maximum number of time slots and

is the maximum number of exits in the networks.…”

Section: Solution Approachesmentioning

confidence: 99%

“…Inside the "while" loop, A* shortest path algorithm must be performed for every emergency exit. When using the binary heap data structure, the time complexity of A* shortest path algorithm is O(|N|log|N|) where N is the set of nodes in the networks [38]. Then the time complexity for the BFS algorithm is O(|T| × |E| × |N|log|N|), where |T| is the maximum number of time slots and |E| is the maximum number of exits in the networks.…”

Section: Three Shortest Path-based Algorithms Without Addressing the Temperature Constraint (21)mentioning

confidence: 99%

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Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

Yen

Lin

Tsao

2020

Sensors

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Temperature sensors with a communication capability can help monitor and report temperature values to a control station, which enables dynamic and real-time evacuation paths in fire emergencies. As compared to traditional approaches that identify a one-shot fire evacuation path, in this paper, we develop an intelligent algorithm that can identify time-aware and temperature-aware fire evacuation paths by considering temperature changes at different time slots in multi-story and multi-exit buildings. We first propose a method that can map three-dimensional multi-story multi-exit buildings into a two-dimensional graph. Then, a mathematical optimization model is proposed to capture this time-aware and temperature-aware evacuation path problem in multi-story multi-exit buildings. Six fire evacuation algorithms (BFS, SP, DBFS, TABFS, TASP and TADBFS) are proposed to identify the efficient evacuation path. The first three algorithms that do not address human temperature limit constraints can be used by rescue robots or firemen with fire-proof suits. The last three algorithms that address human temperature limit constraints can be used by evacuees in terms of total time slots and total temperature on the evacuation path. In the computational experiments, the open space building and the Taipei 101 Shopping Mall are all tested to verify the solution quality of these six algorithms. From the computational results, TABFS, TASP and TADBF identify almost the same evacuation path in open space building and the Taipei 101 Shopping Mall. BFS, SP DBFS can locate marginally better results in terms of evacuation time and total temperature on the evacuation path. When considering evacuating a group of evacuees, the computational time of the evacuation algorithm is very important in a time-limited evacuation process. Considering the extreme case of seven fires in eight emergency exits in the Taipei 101 Shopping Mall, the golden window for evacuation is 15 time slots. Only TABFS and TADBFS are applicable to evacuate 1200 people in the Taipei 101 Shopping Mall when one time slot is setting as one minute. The computational results show that the capacity limit for the Taipei 101 Shopping Mall is 800 people in the extreme case of seven fires. In this case, when the number of people in the building is less than 700, TADBFS should be adopted. When the number of people in the building is greater than 700, TABFS can evacuate more people than TADBFS. Besides identifying an efficient evacuation path, another significant contribution of this paper is to identify the best sensor density deployment at large buildings like the Taipei 101 Shopping Mall in considering the fire evacuation.

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“…Inside the “while” loop, D* shortest path algorithm must be performed for every emergency exit. The time complexity of D* shortest path algorithm is

which is the same as the time complexity of the A* algorithm [ 38 , 39 ]. Then the time complexity for the DBFS algorithm is

, where

is the maximum number of time slots and

is the maximum number of exits in the networks.…”

Section: Solution Approachesmentioning

confidence: 99%

Section: Three Shortest Path-based Algorithms Without Addressing the Temperature Constraint (21)mentioning

confidence: 99%

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

Yen

Lin

Tsao

2020

Sensors

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“…The search-based methods, such as A*, 25 D*, 26 Theta*, 27,28 and L*, 29 are able to find a globally optimal but piecewise-linear path. Some researchers try to smooth the piecewise-linear path by post-processing.…”

Section: Related Workmentioning

confidence: 99%

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

Zhang

Yang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

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“…We separate this part in two: nodes are visited; it can vary widely according to how the searching algorithm is implemented [36,47].…”

mentioning

confidence: 99%

Strength in coalitions: Community detection through argument similarity

Budán

Gonzalez

Budán

et al. 2023

AAC

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We present a novel argumentation-based method for finding and analyzing communities in social media on the Web, where a community is regarded as a set of supported opinions that might be in conflict. Based on their stance, we identify argumentative coalitions to define them; then, we apply a similarity-based evaluation method over the set of arguments in the coalition to determine the level of cohesion inherent to each community, classifying them appropriately. Introducing conflict points and attacks between coalitions based on argumentative (dis)similarities to model the interaction between communities leads to considering a meta-argumentation framework where the set of coalitions plays the role of the set of arguments and where the attack relation between the coalitions is assigned a particular strength which is inherited from the arguments belonging to the coalition. Various semantics are introduced to consider attacks’ strength to particularize the effect of the new perspective. Finally, we analyze a case study where all the elements of the formal construction of the formalism are exercised.

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L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

Cited by 23 publications

References 20 publications

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

Strength in coalitions: Community detection through argument similarity

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