A cellular automata model for indoor evacuation based on artificial potential field and ant colony algorithm

2016 ◽  
pp. 909-913 ◽  
Author(s):  
Zhi-Wei Ye ◽  
Yu-Jie Yin ◽  
Xin-Lu Zong ◽  
Ming-Wei Wang ◽  
Ying-Li Jiang
Keyword(s):  
Cellular Automata ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Artificial Potential Field ◽  
Cellular Automata Model

An improved ant colony algorithm based on artificial potential field and quantum evolution theory

2021 ◽  
pp. 1-16
Author(s):  
Longzhen Zhai ◽  
Shaohong Feng
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Emergency Evacuation ◽  
Ant Colony ◽  
Quantum Evolution ◽  
Artificial Potential Field ◽  
Evolution Theory ◽  
Field Force ◽  
Improved Ant Colony Algorithm

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

Robotica ◽  
2009 ◽  
Vol 28 (6) ◽  
pp. 833-846 ◽  
Author(s):  
Yuan Mingxin ◽  
Wang Sun'an ◽  
Wu Canyang ◽  
Li Kunpeng
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Immune Network ◽  
Network Algorithm ◽  
Dynamic Obstacle Avoidance ◽  
Optimal Dynamic ◽  
Dynamic Obstacle

SUMMARYInspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

scholarly journals Potential Field Cellular Automata Model for Pedestrian Evacuation in a Domain with a Ramp

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiao-Xia Jian ◽  
Xiaoning Zhang
Keyword(s):  
Cellular Automata ◽  
Potential Field ◽  
Empty Space ◽  
Domino Effect ◽  
Cellular Automata Model ◽  
Pedestrian Evacuation ◽  
Crowd Density ◽  
Proposed Model ◽  
The Cost ◽  
The Relationship

We propose a potential field cellular automata model with a pushing force field to simulate the pedestrian evacuation in a domain with a ramp. We construct a cost potential depending on the ramp angle and introduce a function to evaluate the pushing force, which is related to the cost and the desired direction of pedestrian. With increase of crowd density, there is no empty space for pedestrian moving forward; pedestrian will purposefully push another pedestrian on her or his desired location to arrive the destination quickly. We analyse the relationship between the slope of ramp and the pushing force and investigate the changing of injured situations with the changing of the slope of ramp. When the number of pedestrians and the ramp angle arrive at certain critical points, the Domino effect will be simulated by this proposed model.

scholarly journals Potential field cellular automata model for pedestrian flow

2012 ◽  
Vol 85 (2) ◽  
Author(s):  
Peng Zhang ◽  
Xiao-Xia Jian ◽  
S. C. Wong ◽  
Keechoo Choi
Keyword(s):  
Cellular Automata ◽  
Potential Field ◽  
Pedestrian Flow ◽  
Cellular Automata Model
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