scholarly journals Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longhao Yan ◽  
Ping Wang ◽  
Jingwen Yang ◽  
Yu Hu ◽  
Yu Han ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Queue Length ◽  
Traffic Accidents ◽  
Arrival Time ◽  
Learning Algorithm ◽  
Shaanxi Province ◽  
Emergency Rescue ◽  
Emergency Vehicles ◽  
Urban Arterial

Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents.

Path Planning Collision Avoidance using Reinforcement Learning

2020 ◽  
Author(s):  
Josias G. Batista ◽  
Felipe J. S. Vasconcelos ◽  
Kaio M. Ramos ◽  
Darielson A. Souza ◽  
José L. N. Silva
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Production Process ◽  
Collision Avoidance ◽  
Production Systems ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Trajectory Generation ◽  
Industrial Robots ◽  
Q Learning

Industrial robots have grown over the years making production systems more and more efficient, requiring the need for efficient trajectory generation algorithms that optimize and, if possible, generate collision-free trajectories without interrupting the production process. In this work is presented the use of Reinforcement Learning (RL), based on the Q-Learning algorithm, in the trajectory generation of a robotic manipulator and also a comparison of its use with and without constraints of the manipulator kinematics, in order to generate collisionfree trajectories. The results of the simulations are presented with respect to the efficiency of the algorithm and its use in trajectory generation, a comparison of the computational cost for the use of constraints is also presented.

scholarly journals A Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Video Game ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Basic Knowledge ◽  
Target Point ◽  
Reinforcement Learning Algorithm

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

scholarly journals Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987902 ◽  
Author(s):  
Ronglei Xie ◽  
Zhijun Meng ◽  
Yaoming Zhou ◽  
Yunpeng Ma ◽  
Zhe Wu
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Convergence Speed ◽  
Average Reward ◽  
Heuristic Function ◽  
Experience Replay ◽  
Aerial Vehicle

In order to solve the problem that the existing reinforcement learning algorithm is difficult to converge due to the excessive state space of the three-dimensional path planning of the unmanned aerial vehicle, this article proposes a reinforcement learning algorithm based on the heuristic function and the maximum average reward value of the experience replay mechanism. The knowledge of track performance is introduced to construct heuristic function to guide the unmanned aerial vehicles’ action selection and reduce the useless exploration. Experience replay mechanism based on maximum average reward increases the utilization rate of excellent samples and the convergence speed of the algorithm. The simulation results show that the proposed three-dimensional path planning algorithm has good learning efficiency, and the convergence speed and training performance are significantly improved.

scholarly journals Improved Path Planning for Indoor Patrol Robot Based on Deep Reinforcement Learning

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 132
Author(s):  
Jianfeng Zheng ◽  
Shuren Mao ◽  
Zhenyu Wu ◽  
Pengcheng Kong ◽  
Hao Qiang
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Target Position ◽  
Convergence Speed ◽  
Position Information ◽  
Image Information ◽  
Navigation Task ◽  
Reinforcement Learning Algorithm

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable.

scholarly journals Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yunmei Yuan ◽  
Hongyu Li ◽  
Lili Ji
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
New Technologies ◽  
Learning Algorithm ◽  
Computing Time ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Logistics Industry ◽  
Vehicle Path ◽  
The Impact

Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.

scholarly journals Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyun Lei ◽  
Zhian Zhang ◽  
Peifang Dong
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Learning Algorithm ◽  
Dynamic Environment ◽  
Target Position ◽  
Dynamic Environments ◽  
Unknown Environment ◽  
Starting Position ◽  
Dynamic Path Planning ◽  
Dynamic Path

Dynamic path planning of unknown environment has always been a challenge for mobile robots. In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment. The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space. In different training stages, we dynamically adjust the starting position and target position. With the updating of neural network and the increase of greedy rule probability, the local space searched by agent is expanded. Pygame module in PYTHON is used to establish dynamic environments. Considering lidar signal and local target position as the inputs, convolutional neural networks (CNNs) are used to generalize the environmental state. Q-learning algorithm enhances the ability of the dynamic obstacle avoidance and local planning of the agents in environment. The results show that, after training in different dynamic environments and testing in a new environment, the agent is able to reach the local target position successfully in unknown dynamic environment.

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