I. INTRODUCTION

II. PROPOSED SCHEME

III. PERFORMANCE EVALUATION

IV. CONCLUSION

REFERENCES

In smart cities and factories, robotic applications require high dexterity and security, which requires precise inverse dynamics model. However, the physical modeling methods cannot model the uncertain factors of the manipulator such as flexibility, joint clearance and friction, etc. As an alternative, artificial intelligence (AI) techniques have become increasingly popular in robotics for smart cities and factories. In this paper, deep learning neural network based on LSTM (Long Short-Term Memory) is adopted to predict the manipulator inverse dynamics. This study aims to summarize the influence of the hyper-parameter settings on model performance and to explore the applicability of the LSTM model to joint torque prediction of multiple degrees of freedom series manipulator. Furthermore, the feasibility of using only joint position as input data for torque prediction is verified. Simulation result has shown that, for the proposed deep learning architecture, the effects of the number of maximum epochs on model performance should be prioritized. The effects of the number of hidden nodes on model performance are limited, while prediction accuracy will deteriorate as the number of hidden layers increases. It is proved that it is feasible to predict inverse dynamics when input data is joint position only. The experimental results show that the training time increases with the increase of hidden layers, neurons and epochs.

INTRODUCTION

Smart city is an intelligent city based on internet of things, cloud computing and artificial intelligence technology. It adopts advanced information technology, analyzes the trend of the city, makes quick intelligent responses to urban planning, livelihood policies, social security and other aspects, and realizes the intelligent management of the city. At present, there are many urban problems, such as air pollution, water pollution, garbage pollution, shortage of resources, traffic jam and so on. These problems seriously affect people’s life and hinder the development of the city. To solve these problems, it is necessary to build smart cities to improve people’s way of life, create a beautiful life and environment, and promote urban development and innovation. Similar to smart city, smart factory is composed of many intelligent manufacturing equipments (including control and information systems), namely several intelligent branches and equipment that is composed of various intelligent components. Robots used for smart cities and factories have accomplished some easy tasks in structured settings that still require fences between the robots and human to ensure safety. Ideally, robots should be able to work side by side with humans, offering their strength to carry heavy loads while presenting no danger. To achieve this objective, it is necessary to obtain an accurate inverse dynamics model of the robot. Moreover, inverse dynamics has been a valuable piece of information for robotic function such as compliance control, human-robot cooperation, target operation and trajectory planning.