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Advances in Intelligent Robotics Systems Based Machine Learning

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 23126

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Dr. Nicola Pellegrini

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Department of Mechanical and Industrial Engineering, Università degli Studi di Brescia, Via Branze, 38, 25123 Brescia, BS, Italy
Interests: applied mechanics; mechatronics; collaborative robotics; vibration control; reliability and prognostics; robotics integration in manufacturing cells
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Special Issue Information

Dear Colleagues,

Intelligent robotics is one of the boosted applications referred to the confluence of digital technologies as the manufacturing decision support system, the autonomous navigation, the vehicle and drone communication, the self-diagnosis and prognostics actions. Furthermore, intelligent robots need not only robot hardware design and control but also robot vision, robot sensor fusion and human-robot interaction. In particular, advanced mechatronics in combination with Big data (deep learning, data mining, sensor technology, Internet of Things, mechanical and electrical engineering) is providing a wide range of increasingly sophisticated robotic and high-tech systems for practical applications in service and manufacturing industry, health care, marketing, logistics, demotics, security and safety. This motivates researchers to study, design, and develop novel enabling robotics technologies and methods. Intelligent technology must be in line with the human responsibility to ensure the basic sustainability and preservation of the environment within a circular paradigm. The challenge is to transfer the research results and new knowledge to stakeholders, creating a general awareness of the importance of the following engineering optimization topics: energy consumption, reliability, availability, and reuse perspectives. The scope of this Special Issue is to collect high-quality research that reports on recent advances and developments in designing sustainable intelligent robotics focusing on machine learning enabling aptitude, mechatronic devices, and technologies for service robots.

Dr. Nicola Pellegrini
Guest Editor

Manuscript Submission Information

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Keywords

machine learning enabling practices
energy efficiency in robotized layout and data processing
sensor for challenging applications
robot perception and cognition
residual useful life paradigm driven by data mining
robot human interaction
big data platform

Published Papers (6 papers)

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Research

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20 pages, 4699 KiB

Open AccessArticle

Lower Limb Exoskeleton for Rehabilitation with Flexible Joints and Movement Routines Commanded by Electromyography and Baropodometry Sensors

by Yukio Rosales-Luengas, Karina I. Espinosa-Espejel, Ricardo Lopéz-Gutiérrez, Sergio Salazar and Rogelio Lozano

Sensors 2023, 23(11), 5252; https://doi.org/10.3390/s23115252 - 01 Jun 2023

Cited by 2 | Viewed by 1462

Abstract

This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user’s intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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21 pages, 2625 KiB

Open AccessArticle

Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit

by Adenrele A. Ishola and Damian Valles

Sensors 2023, 23(10), 4628; https://doi.org/10.3390/s23104628 - 10 May 2023

Cited by 1 | Viewed by 1520

Abstract

Firefighters face numerous challenges when entering burning structures to rescue trapped victims, assess the conditions of a residential structure, and extinguish the fire as quickly as possible. These challenges include extreme temperatures, smoke, toxic gases, explosions, and falling objects, which can hinder their efficiency and pose risks to their safety. Accurate information and data about the burning site can help firefighters make informed decisions about their duties and determine when it is safe to enter and evacuate, reducing the likelihood of casualties. This research presents unsupervised deep learning (DL) to classify the danger levels at a burning site and an autoregressive integrated moving average (ARIMA) prediction model to forecast temperature changes using the extrapolation of a random forest regressor. The DL classifier algorithms provide the chief firefighter with an awareness of the danger levels in the burning compartment. The prediction models forecast the rise in temperature from a height ranging from 0.6 m to 2.6 m and the changes in temperature over time at an altitude of 2.6 m. Predicting the temperature at this altitude is critical as the temperature increases faster with height, and elevated temperatures can weaken the building’s structural material. We also investigated a new classification method using an unsupervised DL autoencoder artificial neural network (AE-ANN). The prediction data analytical approach included using the autoregressive integrated moving average (ARIMA) with random forest regression implementation. The proposed AE-ANN model, with an accuracy score of 0.869, did not perform as well compared to previous work, with an accuracy of 0.989, at achieving high accuracy scores for the classification task using the same dataset. However, the random forest regressor and our ARIMA models are analyzed and evaluated in this work, while other research has not utilized this dataset, even though it is open-sourced. However, the ARIMA model demonstrated remarkable predictions of the trends of temperature changes in a burning site. The proposed research aims to classify fire sites into dangerous levels and predict temperature progression using deep learning and predictive modeling techniques. This research’s main contribution is using a random forest regressor and autoregressive integrated moving average models to predict temperature trends in burning sites. This research demonstrates the potential of using deep learning and predictive modeling to enhance firefighter safety and decision-making processes. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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15 pages, 1713 KiB

Open AccessArticle

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

by Sibo Yang, Neha P. Garg, Ruobin Gao, Meng Yuan, Bernardo Noronha, Wei Tech Ang and Dino Accoto

Sensors 2023, 23(6), 2998; https://doi.org/10.3390/s23062998 - 10 Mar 2023

Cited by 6 | Viewed by 1797

Abstract

The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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15 pages, 1696 KiB

Open AccessArticle

Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments

by Hung Son Nguyen, Francisco Cruz and Richard Dazeley

Sensors 2023, 23(5), 2681; https://doi.org/10.3390/s23052681 - 01 Mar 2023

Viewed by 1463

Abstract

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent’s learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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19 pages, 74071 KiB

Open AccessArticle

Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot

by Muhammad Imad, Oualid Doukhi, Deok Jin Lee, Ji chul Kim and Yeong Jae Kim

Sensors 2022, 22(21), 8101; https://doi.org/10.3390/s22218101 - 22 Oct 2022

Cited by 1 | Viewed by 2252

Abstract

Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles. This study uses semantic segmentation-based drivable area estimation as an alternative representation to assist with local motion planning. Notably, a realistic 3D simulator based on an Unreal Engine was created to generate a synthetic dataset under different weather conditions. A transfer learning technique was used to train the encoder-decoder model to segment free space from the occupied sidewalk environment. The local planner uses a nonlinear model predictive control (NMPC) scheme that inputs the estimated drivable space, the state of the robot, and a global plan to produce safe velocity commands that minimize the tracking cost and actuator effort while avoiding collisions with dynamic and static obstacles. The proposed approach achieves zero-shot transfer from a simulation to real-world environments that have never been experienced during training. Several intensive experiments were conducted and compared with the dynamic window approach (DWA) to demonstrate the effectiveness of our system in dynamic sidewalk environments. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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Review

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35 pages, 610 KiB

Open AccessReview

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

by Dong Han, Beni Mulyana, Vladimir Stankovic and Samuel Cheng

Sensors 2023, 23(7), 3762; https://doi.org/10.3390/s23073762 - 05 Apr 2023

Cited by 21 | Viewed by 13923

Abstract

Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)

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