Hybrid Path Planning of A Quadrotor UAV Based on Q-Learning Algorithm

2018 ◽  
Author(s):  
Tianze Zhang ◽  
Xin Huo ◽  
Songlin Chen ◽  
Baoqing Yang ◽  
Guojiang Zhang
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Q Learning ◽  
Quadrotor Uav

scholarly journals An Improved Q-learning Algorithm for Path-Planning of a Mobile Robot

2012 ◽  
Vol 51 (9) ◽  
pp. 40-46 ◽  
Author(s):  
Pradipta KDas ◽  
S. C. Mandhata ◽  
H. S. Behera ◽  
S. N. Patro
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Q Learning

scholarly journals Autonomous Path Planning Scheme Research for Mobile Robot

2016 ◽  
Vol 16 (4) ◽  
pp. 113-125
Author(s):  
Jianxian Cai ◽  
Xiaogang Ruan ◽  
Pengxuan Li
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Action Learning ◽  
Cognitive Learning ◽  
Learning Ability ◽  
Q Learning ◽  
Planning Strategy ◽  
Navigation Path

Abstract An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

scholarly journals Multi-Robot Path Planning Method Using Reinforcement Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3057 ◽  
Author(s):  
Hyansu Bae ◽  
Gidong Kim ◽  
Jonguk Kim ◽  
Dianwei Qian ◽  
Sukgyu Lee
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Robot Path Planning ◽  
Q Learning ◽  
Efficient Performance ◽  
Planning Algorithm ◽  
Neural Network Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path ◽  
Multi Robot

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

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 Modified Q-Learning Algorithm for Robot Path Planning in a Digital Twin Assembly System

2021 ◽  
Author(s):  
Xiaowei Guoa
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Virtual Space ◽  
Assembly System ◽  
Case Based Reasoning ◽  
Planning Problem ◽  
Q Learning ◽  
Digital Twin ◽  
Product Assembly

Abstract Product assembly is an important stage in complex product manufacturing. How to intelligently plan the assembly process based on dynamic product and environment information has become an pressing issue needs to be addressed. For this reason, this research has constructed a digital twin assembly system, including virtual and real interactive feedback, data fusion analysis and decision-making iterative optimization modules. In the virtual space, a modified Q-learning algorithm is proposed to solve the path planning problem in product assembly. The proposed algorithm speeds up the convergence speed by adding dynamic reward function, optimizes the initial Q table by introducing knowledge and experience through the case-based reasoning (CBR) algorithm, and prevents entry into the trapped area through the obstacle avoiding method. Finally, take the six-joint robot UR10 as an example to verify the performance of the algorithm in the three-dimensional pathfinding space. The experimental results show that the modified Q-learning algorithm's pathfinding performance is significantly better than the original Q-learning algorithm.

scholarly journals Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1797-1807 ◽  
Author(s):  
Niu Guochen ◽  
Xu Kailu
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Initial Point ◽  
Fuel Tank ◽  
Simulation Experiments ◽  
Q Learning ◽  
Inspection Robot ◽  
Computing Complexity ◽  
Aircraft Fuel ◽  
Continuum Robot

In order to realize the path planning of continuum robot for inspecting defects in the aircraft fuel tank compartment, an approach based on Q-learning and Three-segment Method was proposed, and the posture of the robot meeting the inherent and spatial structure constraint requirements was planned. Firstly, the simulation model of the aircraft fuel tank was established. Moreover, the workspace was rasterized to decrease the computing complexity. Secondly, the Q-learning algorithm was applied and the path from the initial point to the target was generated. In terms of target guided angle and three-segment method, the joint variables corresponding to each transition point on the path could be obtained. Finally, the robot reached the target by progressively updating the joint variables. Simulation experiments were implemented, and the results verified the effectiveness and feasibility of the algorithm.

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