Aiming at the danger and unsatisfactory effect of dense crowd evacuation in public places in emergency, a motion planning algorithm of robots for crowd evacuation based on Deep Q-Network (DQN) was proposed. Firstly, a human-robot social force model was constructed by adding human-robot interaction to the original social force model, so that the motion state of crowd was able to be influenced by the robot force on pedestrians. Then, a motion planning algorithm of robot was designed based on DQN. The images of the original pedestrian motion state were input into the network and the robot motion behavior was output. In this process, the designed reward function was fed back to the network to enable the robot to autonomously learn from the closed-loop process of "environment-behavior-reward". Finally, the robot was able to learn the optimal motion strategies at different initial positions to maximize the total number of people evacuated after many iterations. The proposed algorithm was trained and evaluated in the simulated environment. Experimental results show that the proposed algorithm based on DQN increases the evacuation efficiency by 16.41%, 10.69% and 21.76% respectively at three different initial positions compared with the crowd evacuation algorithm without robot, which proves that the algorithm can significantly increase the number of people evacuated per unit time with flexibility and effectiveness.

To deal with the difficulty and low performance of emergency crowd evacuation in public spaces, a crowd evacuation method using robots based on the social force model was proposed. A new human-robot social force model based on the original social force model was first developed, where the human-robot interaction from robots to pedestrians was added to the original social force model. And then, a new method using robot based on the human-robot social force model was presented to evacuate the crowd. After joining the crowd evacuation scenes, the robots can influence the motion of the surrounding pedestrians and reduce the pressure among the pedestrians by moving in the crowd, thus increasing the crowd motion speed and improving the efficiency of crowd evacuation. Two classical scenarios, including a group of crowd escaping from a closed environment and two groups of crowd moving to each other by crossing, were designed and simulated to test the proposed method, and the crowd evacuation method without robots was used for comparison. The experimental results demonstrate that the proposed method based on human-robot social force model can obviously increase the crowd motion speed and improve the efficiency of crowd evacuation.