STUDY ON DETOUR PATH DERIVATION FOR 7-AXIS ROBOT USING DEEP REINFORCEMENT LEARNING
نویسندگان
چکیده
The 7-axis articulated robot can arbitrarily rotate the position of elbow by changing angle 7th axis. When a makes detour, it is necessary to properly control both and orientation tool E-axis angle. In this study, we propose deep reinforcement learning method for automatically generating detour paths including
منابع مشابه
Reinforcement Learning in Robot Path Optimization
Along with the development of robot technology, a robot not only need to complete a specific task, but aslo need to do path planning in the process of performing the task. So, path planning is widly studied. This paper introduce a method of robot path planning based on reinforcement learning,which aimed at Markovian decision process. In this paper, we introduce the basic concept, principle and ...
متن کاملTraining an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions. We describe a learning approach as a step in this direction, where we teach a humanoid robot how to play the game of noughts and crosses. Given that multiple multimodal skills can be t...
متن کاملOperation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملOn Improving Deep Reinforcement Learning for POMDPs
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to enhance learn...
متن کاملPath Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning
In this paper path planning and obstacle avoidance for a statically stable biped robot using PRM and reinforcement learning is discussed. The main objective of the paper is to compare these two methods of path planning for applications involving a biped robot. The statically stable biped robot under consideration is a 4-degree of freedom walking robot that can follow any given trajectory on fla...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Aij Journal of Technology and Design
سال: 2022
ISSN: ['1341-9463', '1881-8188']
DOI: https://doi.org/10.3130/aijt.28.1602