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In civil aviation flight path planning, in order to effectively reduce the safety threat caused by the volcanic ash area to the civil aviation flight, factors such as the speed and acceleration of the aircraft in the volcanic ash area must be considered. In this paper, we propose an improved A-star algorithm by adopting the concept of potential col...
Citations
... Escape path planning is a prerequisite for crowd evacuation simulation and is used to help evacuees find suitable evacuation routes (Wang et al. 2021d;Li and Zhang 2022a). The main escape path planning methods are traditional path planning methods, intelligent bionic algorithms, heuristic search algorithms, and navigation grid-based pathfinding methods (Charalambopoulos and Nearchou 2021;Ma et al. 2022;Zhai and Feng 2022). Traditional path planning methods mainly utilise 2D static graphs to achieve geometric path search, but they cannot be adapted to 3D scenes. ...
Crowd evacuation simulation using virtual reality (VR) is significant for digital emergency response construction. However, existing evacuation simulation studies suffer from poor adaptation to complex environments, inefficient evacuations, and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation. To more realistically express the crowd evacuation results obtained under the influence of fire environments and the subjective consciousness of pedestrians in subway stations, we designed a dynamic pedestrian evacuation path planning method under multiple constraints, analysed the influences of an ‘environmental role’ and a ‘subjective initiative’ on crowd evacuation, established an improved social force model (ISFM)-based crowd evacuation simulation method in VR, developed a prototype system and conducted experimental analyses. The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity. In simulation experiments without disaster scenarios, the improved model's crowd evacuation efficiency improved by averages of 12.53% and 15.37% over the commercial Pathfinder software and the original social force model, respectively. The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subway fires.
... Solving the inbound task of the shuttle from the lift entrance to the cargo path or the outbound task of the cargo path to the lift entrance can be transformed into the problem of solving the shortest path in a directed acyclic weighted graph. Based on the comprehensive shuttle physical structure of the characteristics of the warehouse, this paper selects the heuristic A * algorithm [43][44][45][46] , using the Euclidean distance as the valuation function calculation, which can avoid dropping invalid points and accelerate the finding efficiency. Because the warehouse construction is completed and its four-way shuttle physical track is fixed, the path finding task calculation of the four-way shuttle is accelerated by statically initialising the graph of the road network in each layer when the system is started, and only the weights of the arcs that can be directly reached from the starting position of the four-way shuttle need to be amended. ...
In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the four-way shuttle system. An improved genetic algorithm is used to solve the task planning, and an improved A* algorithm is used to solve the path optimization within the shelf level. The conflicts generated by the parallel operation of the four-way shuttle system are classified, and the improved A* algorithm based on the time window method is constructed for path optimization through the dynamic graph theory method to seek safe conflict-free paths. Through simulation example analysis, it is verified that the improved A* algorithm proposed in this paper has obvious optimization effect on the model of this paper.
... Li et al. [21]., improved the genetic algorithm with A-star algorithm. Ma et al. [22]., adopted the idea of collision ji to improve A-star algorithm. Li et al. [23]., proposed an improved A-star algorithm combined with the jump point search algorithm to reduce the computational cost of A-star algorithm. ...
To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5DOS model is proposed in this paper. The direction layer of the R5DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance.
... In the application scenario of AUV, the complex and dense dynamic obstacles of an uncertain environment pose a huge challenge to the safe navigation of AUV. Traditional obstacle avoidance methods (such as the A* algorithm, artificial potential field method, Voronoi diagram, RRT algorithm, swarm intelligence algorithm [5][6][7][8][9] , etc.) are all used to avoid obstacles when the environmental information is known. However, due to the dense dynamic obstacles in the uncertain environment, AUV cannot obtain the motion information of dynamic obstacles in advance, and traditional methods cannot be effectively applied to real-time obstacle avoidance. ...
A reasonable obstacle avoidance method for AUV 3D path planning is difficult. Existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, this study designs an obstacle collision prediction model (CPM). Based on the results of the simulation operation of the obstacle inertial motion, the safety of the AUV navigation is evaluated to improve the sensitivity of the model to dynamic obstacles. Then, the learning ability of the sequence sample data is enhanced by combining it with the long short-term memory (LSTM) network, and the training efficiency and effect of the algorithm are improved. The trained proximal policy optimization (PPO) network can output reasonable actions to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments, and successfully realizes dynamic obstacle avoidance of AUV in different three-dimensional unknown environments, providing theoretical and technical support for real path planning.
To solve the problems of unsmooth path planning, insufficient dynamic obstacle avoidance ability, and environmental disturbance effect on the path planning result, this paper proposes a smooth path planning method for unmanned surface vessels (USVs) considering environmental disturbance. First, an improved A* algorithm, which uses the path smoothing method based on the minimum turning radius of a USV, is proposed for global path planning. The binary tree method is used instead of the enumeration method to select a relatively optimal path in the current situation to improve algorithm efficiency. In addition, the dynamic window approach (DWA) with the Convention on the International Regulation for Preventing Collision at Sea (COLREGs) constraints is used for local path planning. The dist function in the DWA algorithm is improved to enhance the DWA algorithm’s ability to avoid dynamic obstacles. Finally, the environmental disturbance function is derived and added to the A* and DWA algorithms to handle the effect of environmental disturbances, such as water flow, on the path planning result, which can significantly improve the path-planning ability of the algorithm in the presence of environmental disturbances. Simulation experiments are performed in three scenarios to verify the proposed algorithm. The experimental results show that compared with the other algorithms, the proposed algorithm can effectively avoid dynamic obstacles and reduce the impact of environmental disturbance on the path planning result. At the same time, the proposed algorithm has high efficiency and strong robustness.
