Intelligent Robotic Control System Based on Computer Vision Technology

E. Hamza P. Yap A. Osman + 6 lainnya

9 Mei 2023

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

M. Abdulhasan Salama A. Mostafa S. S. Chopra + 6 lainnya

28 Juli 2022

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

Akey Sungheetha

22 Februari 2021

In various real time applications, several assisted services are provided by the human-robot interaction (HRI). The concept of convergence of a three-dimensional (3D) image into a plane-based projection is used for object identification via digital visualization in robotic systems. Recognition errors occur as the projections in various planes are misidentified during the convergence process. These misidentifications in recognition of objects can be reduced by input processing scheme dependent on the projection technique. The conjoining indices are identified by projecting the input image in all possible dimensions and visualizing it. Machine learning algorithm is used for improving the processing speed and accuracy of recognition. Labeled analysis is used for segregation of the intersection without conjoined indices. Errors are prevented by identifying the non-correlating indices in the projections of possible dimension. The inputs are correlated with related inputs that are stored with labels thereby preventing matching of the indices and deviations in the planes. Error, complexity, time and recognition ratio metrics are verified for the proposed model.

Fidel Esquivel Estay Tizian Jermann H. Kolvenbach + 2 lainnya

20 September 2024

We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the waste removal potential. We realize this by formulating the sorting problem as an OpenAI gym environment and training a neural network with a deep reinforcement learning algorithm. The objective function is set up to optimize the picking rate of the robotic system. In simulation, we draw a performance comparison to an intuitive combinatorial game theory-based approach. We show that the trained policies outperform the latter and achieve up to 16% higher picking rates. Finally, the respective algorithms are validated on a hardware setup consisting of a two-robot sorting station able to process incoming waste objects through pick-and-place operations.

Jing Zhao Feng Xie

30 April 2020

Artificial intelligence is an important branch of cognitive science. Artificial intelligence has been applied to medical, education, logistics and other industries, which has broad prospects for development. Hotel logistics industry uses artificial intelligence technology to complete intelligent search, face recognition, combined with large data calculation and planning reasonable path in warehousing. Taking China's hotel logistics industry as the research object, this paper analyses the application of artificial intelligence technology in the logistics industry. In the warehousing process, artificial intelligence technologies including compile storage code, automatic picking with automated guided vehicle, warehouse robot to improve work efficiency. Logistics distribution links use artificial intelligence technology to plan the best path, improve the recognition rate of express waybill that save a lot of labour. Artificial intelligence technology allocates logistics resources, optimises logistics links, and improves logistics efficiency and other measures to promote the development of logistics informatisation and automation.