Virtual Hoisting Path Planning Based on Improved Probabilistic RoadMap Algorithm

Wang, Wei; Wang, Jianfeng; Qu, Jue; Rao, C. S. P.; Yang, Fan

doi:10.3724/sp.j.1089.2018.16441

Cited by 2 publications

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“…Global path planning represents the collision‐free space as a graph and finds an optimal path to the goal while avoiding collisions with static obstacles based on the graph search algorithm, in which the environment is known and unchanged. According to different ways of graph representation, global path planning can be divided into A* algorithm 2 and the combination of A* with data‐driven approach, 3 probabilistic roadmaps, 4 navigation meshes, 5 navigation graphs, 6 and Voronoi diagrams 7 . Local collision avoidance steers the preferred velocity away from collisions with other agents in which the environment is unknown or known partially.…”

Section: Related Workmentioning

confidence: 99%

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

Sun

Qin

2022

Computer Animation & Virtual

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We propose a virtual crowd navigation approach based on deep reinforcement learning to improve the adaptability of virtual crowds in an unknown and complex environment. To address the problem of local optimum or slow iteration or even failure to converge due to sparse rewards in complex environments, we integrate the curiosity-driven mechanism, the key navigation points acquisition and the failure path penalty method in addition to combining long short-term memory networks, dynamic obstacle collision prediction with proximal policy optimization algorithms, which realizes the crowd navigation in a complex environment. The experimental results show that the proposed approach can simulate the motions of virtual crowds in various dynamic and complex scenarios without the environment modeling. The use of continuous action space also ensures that the movement trajectories of the virtual crowds are more realistic and natural. Furthermore, our approach can provide analysis and demonstration tools for a variety of independent collaborations such as competition and cooperation of group intelligence in an open and dynamic environment.

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Section: Related Workmentioning

confidence: 99%

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

Sun

Qin

2022

Computer Animation & Virtual

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“…As a complex system engineering, urban road traffic intelligent control mainly involves disciplines including automation control, engineering technology engineering, system engineering, and optimal dispatching [18]. Because it has many factors such as randomness, complexity, and uncertainty, people cannot establish an accurate mathematical model, so they must rely on intelligent methods [19]. In the transportation system, urban transportation is a very important part.…”

Section: Introductionmentioning

confidence: 99%

Research on the Intelligent Assignment Model of Urban Traffic Planning Based on Optimal Path Optimization Algorithm

Lü

Yao

2022

Scientific Programming

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Congestion and complexity in the field of highway transportation have risen steadily in recent years, particularly because the growth rate of vehicles has far outpaced the growth rate of roads and other transportation facilities. To ensure smooth traffic, reduce traffic congestion, improve road safety, and reduce the negative impact of air pollution on the environment, an increasing number of traffic management departments are turning to new scientifically developed technology. The urban road traffic is simulated by nodes and sidelines in this study, which is combined with graph theory, and the information of real-time changes of road traffic is added to display and calculate the relevant data and parameters in the road. On this foundation, the dynamic path optimization algorithm model is discussed in the context of high informationization. Although the improved algorithm’s optimal path may not be the conventional shortest path, its actual travel time is the shortest, which is more in line with users’ actual travel needs to a large extent.

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Virtual Hoisting Path Planning Based on Improved Probabilistic RoadMap Algorithm

Cited by 2 publications

References 0 publications

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

Research on the Intelligent Assignment Model of Urban Traffic Planning Based on Optimal Path Optimization Algorithm

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