UCAV Path Planning Algorithm Based on Deep Reinforcement Learning

2019 ◽  
pp. 702-714
Author(s):  
Kaiyuan Zheng ◽  
Jingpeng Gao ◽  
Liangxi Shen
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path Planning Algorithm for Two-dimensional Raster Maps based on Deep Reinforcement Learning

2021 ◽  
Author(s):  
Jie Li ◽  
Yuhan Zhang ◽  
Jiaqi Tang ◽  
Xianjie Liu ◽  
Abdulhamid Ibrahim
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Two Dimensional ◽  
Raster Maps ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Unmanned Aerial Vehicle Path Planning Algorithm Based on Deep Reinforcement Learning in Large-Scale and Dynamic Environments

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 24884-24900
Author(s):  
Ronglei Xie ◽  
Zhijun Meng ◽  
Lifeng Wang ◽  
Haochen Li ◽  
Kaipeng Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Dynamic Environments ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Vehicle Path ◽  
Path Planning Algorithm

scholarly journals Collision Avoidance for a Car-like Mobile Robots using Deep Reinforcement Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 22-30
Author(s):  
Kiwon Yeom ◽  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Time Step ◽  
Learning Path ◽  
Angular Velocities ◽  
Planning Algorithm ◽  
Path Planning Algorithm

—The applications of mobile robots are more and more diverse and extensive. The motion planning of the mobile robots should be considered in aspect of effectiveness of the navigation, and collision-free motion is essential for mobile robots. In addition, dynamic path planning of unknown environment has always been a challenge for mobile robots. Aiming at navigation problems, this paper proposes a Deep Reinforcement Learning (DRL) based path planning algorithm which can navigate nonholonomic car-like mobile robots in an unknown dynamic environment. The output of the learned network are the robot’s translational and angular velocities for the next time step. The method combines path planning on a 2D grid with reinforcement learning and does not need any supervision. The experiments illustrate that our trained policy can be applied to solve complex navigation tasks. Furthermore, we compare the performance of our learned controller to the popular approaches. Keywords— Deep reinforcement learning, path planning, , artificial neural network, mobile robot, autonomous vehicle

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.

Energy management strategy of intelligent plug-in split hybrid electric vehicle based on deep reinforcement learning with optimized path planning algorithm

2021 ◽  
pp. 095440702110368
Author(s):  
Shengguang Xiong ◽  
Yishi Zhang ◽  
Chaozhong Wu ◽  
Zhijun Chen ◽  
Jiankun Peng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Planning Algorithm ◽  
Hybrid Electric ◽  
Path Planning Algorithm

Energy management is a fundamental task and challenge of plug-in split hybrid electric vehicle (PSHEV) research field because of the complicated powertrain and variable driving conditions. Motivated by the foresight of intelligent vehicle and the breakthroughs of deep reinforcement learning framework, an energy management strategy of intelligent plug-in split hybrid electric vehicle (IPSHEV) based on optimized Dijkstra’s path planning algorithm (ODA) and reinforcement learning Deep-Q-Network (DQN) is proposed to cope with the challenge. Firstly, a gray model is used to predict the traffic congestion of each road and the length of each road calculated in the traditional Dijkstra’s algorithm (DA) is modified for path planning. Secondly, on the basis of the predicted velocity of each road, the planned velocity is constrained by the vehicle dynamics to ensure the driving security. Finally, the planning information is inputted to DQN to control the working mode of IPSHEV, so as to achieve energy saving of the vehicle. The simulation results show the optimized path planning algorithm and proposed energy management strategy is feasible and effective.

Sign in / Sign up

Export Citation Format

Share Document