Research on Multi-robot Path Planning Methods Based on Learning Classifier System with Gradient Descent Methods

2012 ◽  
pp. 229-234
Author(s):  
Jie Shao ◽  
JunPeng Zhang ◽  
ChengDong Zhao
Keyword(s):  
Path Planning ◽  
Gradient Descent ◽  
Descent Methods ◽  
Robot Path Planning ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Gradient Descent Methods ◽  
Planning Methods ◽  
Robot Path

scholarly journals Is Gradient Descent Update Consistent with Accuracy-Based Learning Classifier System?

2009 ◽  
Vol 13 (6) ◽  
pp. 640-648
Author(s):  
Atsushi Wada ◽  
◽  
Keiki Takadama ◽  
◽  
Keyword(s):  
Adaptive Systems ◽  
Gradient Descent ◽  
Benchmark Problems ◽  
Gradient Term ◽  
Rule Discovery ◽  
Discovery Process ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier

Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical online learning. An analysis of the reinforcement process of XCS, one of the currently mainstream LCSs, is performed from the aspect of RL. Upon comparing XCS's update method with gradient-descent-based parameter update in RL, differences are found in the following elements: (1) residual term, (2) gradient term, and (3) payoff definition. All possible combinations of the variants in each element are implemented and tested on multi-step benchmark problems. This revealed that few specific combinations work effectively with XCS's accuracy-based rule-discovery process, while pure gradient-descent-based update showed the worst performance.

Multi-Robot Path Planning Based on Learning Classifier System with Policy Gradient Reinforcement Learning and Support Vector Machine (PGRL-SVM)

2013 ◽  
Vol 347-350 ◽  
pp. 3208-3211
Author(s):  
Qiu Li Song ◽  
Jian Bao Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Search Space ◽  
Learning System ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Classifier System ◽  
Learning Space ◽  
Classifier System ◽  
Learning Classifier

This paper presented a novel approach to solving the problem of robot avoidance collision planning. A Learning Classifier System is a accuracy-based machine learning system using gradient descent that combines covering operator and genetic algorithm. The covering operator is responsible for adjusting precision and large search space according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better path planning rules. The advantages of this approach are its accuracy-based representation, that can be easily reduce learning space,online learning ability,robustness due to the use of genetic algorithm.

A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm

2021 ◽  
Author(s):  
Weifei Hu ◽  
Feng Tang ◽  
Zhenyu Liu ◽  
Jianrong Tan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Short Term Memory ◽  
Computational Cost ◽  
Random Tree ◽  
Robot Path Planning ◽  
Path Information ◽  
Planning Methods ◽  
Robot Path ◽  
Path Quality

Abstract As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.

scholarly journals An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-27 ◽  
Author(s):  
Ben Beklisi Kwame Ayawli ◽  
Ryad Chellali ◽  
Albert Yaw Appiah ◽  
Frimpong Kyeremeh
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Planning Problem ◽  
Ground Vehicles ◽  
Robot Path Planning ◽  
Autonomous Ground Vehicles ◽  
Planning Methods ◽  
Robot Path

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

Numerical Potential Field Techniques for Robot Path Planning.

1989 ◽  
Author(s):  
Jerome Barraquand ◽  
Bruno Langlois ◽  
Jean-Claude Latombe
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Robot Path Planning ◽  
Field Techniques ◽  
Robot Path

Topology optimization techniques for mobile robot path planning

2019 ◽  
Vol 78 ◽  
pp. 528-544 ◽  
Author(s):  
Baotong Li ◽  
Honglei Liu ◽  
Wenjun Su
Keyword(s):  
Topology Optimization ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimization Techniques ◽  
Robot Path Planning ◽  
Robot Path
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