scholarly journals Research on Path Planning Algorithm for Crowd Evacuation

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1339
Author(s):  
Zhenfei Wang ◽  
Chuchu Zhang ◽  
Junfeng Wang ◽  
Zhiyun Zheng ◽  
Lun Li
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Large Scale ◽  
Path Selection ◽  
Limited Area ◽  
Evacuation Time ◽  
Planning Algorithm ◽  
Crowd Evacuation ◽  
Avoidance Strategy ◽  
Path Planning Algorithm

In recent years, crowded stampede incidents have occurred frequently, resulting in more and more serious losses. The common cause of such incidents is that when large-scale populations gather in a limited area, the population is highly unstable. In emergency situations, only when the crowd reaches the safe exit as soon as possible within a limited evacuation time to complete evacuation can the loss and casualties be effectively reduced. Therefore, the safety evacuation management of people in public places in emergencies has become a hot topic in the field of public security. Based on the analysis of the factors affecting the crowd path selection, this paper proposes an improved path-planning algorithm based on BEME (Balanced Evacuation for Multiple Exits). And pedestrian evacuation simulation is carried out in multi-exit symmetrical facilities. First, this paper optimizes the update method of the GSDL list in the BEME algorithm as the basis for evacuating pedestrians to choose an exit. Second, the collision between pedestrians is solved by defining the movement rule and collision avoidance strategy. Finally, the algorithm is compared with BEME and traditional path-planning algorithms. The results show that the algorithm can further shorten the global evacuation distance of the symmetrical evacuation scene, effectively balance the number of pedestrians at each exit and reduce the evacuation time. In addition, this improved algorithm uses a collision avoidance strategy to solve the collision and congestion problems in path planning, which helps to maximize evacuation efficiency. Whether the setting of the scene or the setting of the exit, all studies are based on symmetric implementation. This is more in line with the crowd evacuation in the real scene, making the experimental results more meaningful.

Autonomous Collision Imminent Steering Maneuver in Presence of Surrounding Obstacles

2016 ◽  
Author(s):  
Rouhollah Jafari ◽  
Shuqing Zeng ◽  
Nikolai Moshchuk
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Geometric Method ◽  
Target Vehicle ◽  
Virtual Target ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Collision Avoidance System ◽  
Path Planning Algorithm

In this paper, a collision avoidance system is proposed to steer away from a leading target vehicle and other surrounding obstacles. A virtual target lane is generated based on an object map resulted from perception module. The virtual target lane is used by a path planning algorithm for an evasive steering maneuver. A geometric method which is computationally fast for real-time implementations is employed. The algorithm is tested in real-time and the simulation results suggest the effectiveness of the system in avoiding collision with not only the leading target vehicle but also other surrounding obstacles.

Research on Logistics Distribution Path Planning Algorithm Based on GIS

2014 ◽  
Vol 513-517 ◽  
pp. 1871-1874
Author(s):  
Tian Yang Su ◽  
Da Shen Xue
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Path Selection ◽  
Logistics Industry ◽  
Mountain Climbing ◽  
Logistics Distribution ◽  
Advantages And Disadvantages ◽  
Current Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Algorithm of vehicle scheduling optimization could be integrated in the GIS platform. Therefore, distribution software can automatically make the delivery plan and managers also can make the optimizing choice of the optimal distribution route. Firstly, this paper introduces the necessity of introducing GIS into the logistics industry. Moreover, advantages and disadvantages of the current path planning in the logistics distribution which often used in some algorithms (genetic algorithm, mountain climbing algorithm, ant colony algorithm, etc.) will be listed. Finally, a more practical hybrid algorithm will be used to the GIS so that managers can optimize the logistics distribution path selection.

scholarly journals Design, path planning improvement and test of a portable massage robot on human back

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878663 ◽  
Author(s):  
Wendong Wang ◽  
Peng Zhang ◽  
Chaohong Liang ◽  
Yikai Shi
Keyword(s):  
Path Planning ◽  
Input Signal ◽  
Duty Cycle ◽  
Electromagnetic Force ◽  
Large Scale ◽  
Human Life ◽  
Electric Circuit ◽  
Coil Current ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A massage robot that helps to improve the quality of human life has attracted more interests of researchers and consumers. A portable back massage robot that is compact and space-saving was designed to be used on human back instead of a traditional large-scale structure robot. To design the massage robot, the models of electric circuit, magnetic circuit and mechanics were analyzed to achieve optimal massage force. Parameters of the massage actuator are determined based on the influence analysis of the coil current, the coil turns and the distance between the moving core and the yoke on the electromagnetic force. The massage coverage of human back, which is used to calculate the massage effect, could be improved by an excellent path planning algorithm. This article proposed an efficient full covered path planning algorithm for the designed massage robot, and the relevant algorithm models were established. Simulation results show that the coil current is much more sensitive to electromagnetic force of the moving core compared to the other two factors, and the presented path planning algorithm completes full coverage of the massage robot on the back area. The experimental platform of the massage robot was built, and the influence of the input signal duty cycle, the input signal voltage and the hardness of the massage object on the massage effect was discussed by testing the values of acceleration. The tested results show that the massage effect is best when the duty cycle is in the range of 1/8–1/2. Meanwhile, the hardness of massage parts affects the massage intensity. The consistency between the tested results of path planning and simulation verifies the feasibility of the simulation procedure and indicates that the massage robot can attain the desired massage performance and realize the planned paths.

scholarly journals Rapid Planning of an Assembly Path by Reusing the Prior Path

2021 ◽  
Vol 11 (2) ◽  
pp. 633
Author(s):  
Guodong Yi ◽  
Chuanyuan Zhou ◽  
Yanpeng Cao ◽  
Hangjian Hu
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Minimum Cost ◽  
Path Selection ◽  
Selection Strategy ◽  
Fusion Strategy ◽  
Planning Algorithm ◽  
The One ◽  
Path Planning Algorithm ◽  
Start Node

Assembly path planning of complex products in virtual assembly is a necessary and complicated step, which will become long and inefficient if the assembly path of each part is completely planned in the assembly space. The coincidence or partial coincidence of the assembly paths of some parts provides an opportunity to solve this problem. A path planning algorithm based on prior path reuse (PPR algorithm) is proposed in this paper, which realizes rapid planning of an assembly path by reusing the planned paths. The core of the PPR algorithm is a dual-tree fusion strategy for path reuse, which is implemented by improving the rapidly exploring random tree star (RRT *) algorithm. The dual-tree fusion strategy is used to find the nearest prior node, the prior connection node, the nearest exploring node, and the exploring connection node and to connect the exploring tree to the prior tree after the exploring tree is extended to the prior space. Then, the optimal path selection strategy is used to calculate the costs of all planned paths and select the one with the minimum cost as the optimal path. The PPR algorithm is compared with the RRT * algorithm in path planning for one start node and multiple start nodes. The results show that the total time and the number of sampling points for assembly path planning of batch parts using the PPR algorithm are far less than those using the RRT * algorithm.

scholarly journals An Intelligent Path Selection Algorithm Based on Deep Reinforcement Learning

2021 ◽  
Vol 2078 (1) ◽  
pp. 012023
Author(s):  
Mengchen Sun
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Path Selection ◽  
Learning Ability ◽  
Continuous Learning ◽  
Selection Algorithm ◽  
Planning Algorithm ◽  
Intelligent Devices ◽  
Simulation Results ◽  
Path Planning Algorithm

Abstract Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy.

scholarly journals iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

2021 ◽  
Vol 11 (9) ◽  
pp. 3948
Author(s):  
Aye Aye Maw ◽  
Maxim Tyan ◽  
Tuan Anh Nguyen ◽  
Jae-Woo Lee
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Real World ◽  
Mission Planning ◽  
Planning System ◽  
Local Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

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