scholarly journals Reinforcement Learning Path Planning Method with Error Estimation

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Feihu Zhang ◽  
Can Wang ◽  
Chensheng Cheng ◽  
Dianyu Yang ◽  
Guang Pan
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Error Estimation ◽  
Autonomous Driving ◽  
Planning Method ◽  
Robot Kinematics ◽  
Learning Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Accumulated Error

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

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 Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

2021 ◽  
Vol 11 (9) ◽  
pp. 3909
Author(s):  
Changhyeon Park ◽  
Seok-Cheol Kee
Keyword(s):  
Path Planning ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Campus Environment ◽  
Vehicle Localization ◽  
Time Operation ◽  
National University ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

scholarly journals On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 119863-119879
Author(s):  
Zhaoyu Shou ◽  
Xianying Lu ◽  
Zhengzheng Wu ◽  
Hua Yuan ◽  
Huibing Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Learning Behavior ◽  
Learning Path ◽  
Planning Algorithm ◽  
Collaborative Analysis ◽  
Path Planning Algorithm

scholarly journals Path Planning for Highly Automated Driving on Embedded GPUs

2018 ◽  
Vol 8 (4) ◽  
pp. 35 ◽  
Author(s):  
Jörg Fickenscher ◽  
Sandra Schmidt ◽  
Frank Hannig ◽  
Mohamed Bouzouraa ◽  
Jürgen Teich
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Computing Power ◽  
Planning Algorithm ◽  
Time Requirements ◽  
Evaluation Platform ◽  
Path Planning Algorithm ◽  
Highly Automated Driving

The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in ECU are required, such as GPU, because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 SoC was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.

Sign in / Sign up

Export Citation Format

Share Document