Mobile Robot Platform Research Articles

Novel UHF-RFID Listener Hardware Architecture and System Concept for a Mobile Robot Based MIMO SAR RFID Localization

In this paper, we introduce an ultra-high frequency radio frequency identification (UHF-RFID) mobile robot platform that is capable of performing fully autonomous inventory taking and stocktaking by providing three-dimensional (3D) product maps and thus making possible the concept of a smart warehouse. The proposed novel hardware architecture consists of an eight-channel UHF-RFID-listener for parallel signal phase recovery, including two different carrier leakage suppression circuits and a correlation decoder for each channel for the tag signal, which can handle a backscatter link frequency (BLF) deviation of up to ${\mathrm {22~\%}}$ to decode the tag data. The system also uses eight parallel channels for multiple-input multiple-output (MIMO) localization. For the system evaluation we labeled clothes stored in a warehouse with tags and generated their product map. The proposed localization algorithm is based on a synthetic aperture radar (SAR) MIMO approach that needs exact knowledge of the antenna positions and, therefore, of the driven trajectory. This position is provided by the robot, which takes advantage of a simultaneous localization and mapping (SLAM) algorithm, determining the position with ${\mathrm {1~cm}}$ accuracy while generating two-dimensional (2D) maps of the surroundings. We placed ten tags at known positions to assess the system’s performance and were able to locate these tags within a root mean square error (RMSE) of ${\mathrm {1.45~cm}}$ in 3D.

Prototyping Using a Mobile Robot Platform Equipped with Low-End In-Wheel Motors

In the near future, autonomous mobile robots are expected to operate effectively in various locations, such as in living spaces as well as industrial establishments. Against this background, a new autonomous mobile robot platform was designed and prototyped in this research. For simplicity of design and easy assembly of the drive units, a robot with two low-end in-wheel motors is considered. It is also effective in saving space, and can be used for high-power operations and travelability in various road surface conditions. This paper presents a concept for developing a new type of autonomous mobile robot platform, its control system for autonomous operation, actual prototyping using this platform, and sample applications of this platform.

Blindly Controlled Magnetically Actuated Capsule for Noninvasive Sampling of the Gastrointestinal Microbiome

The objective of this article is to address the challenges of physical sample retrieval from all locations along patients' gastrointestinal (GI) tracts for studying of interactions between GI microbial communities (microbiomes) and host immune systems. We propose a novel tetherless, blindly controlled, magnetically actuated capsule for noninvasive sampling of GI microbiomes and liquid digesta. The mesoscale capsule, a soft mobile robot platform, is made from two permanent magnets encapsulated in a soft elastomer and actuated remotely by an external magnet. The capsule is controlled blindly using an external magnet to orient and activate the capsule. Following oral administration, capsules transit the GI tract and are activated at specified locations. The capsule is recovered via routine stool passage and collected samples are processed for downstream analyses (e.g., 16S rRNA surveys, metagenomics, metatranscriptomics and/or metabolomics). The localization of the capsule is estimated using the traveling time and the location of collected microbiomes will be confirmed by downstream analyses. A mathematical model of capsule activation is used to predict capsule activation and guide design optimization. We demonstrate the ability of the capsule to collect samples, as well as maintain sample integrity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro, ex vivo</i> , and with twelve capsules in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> swine models. Eleven capsules successfully collected digesta (in the range of 18 to 61 mg). This sampling apparatus offers a technological advance for the robust sampling of GI tract contents.

A dual-arm mobile robot system performing assistive tasks operated via P300-based brain computer interface

PurposeIn the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.Design/methodology/approachThe system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.FindingsThe effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.Originality/valueThe P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

Open-source embedded Linux mobile robot platform for Mechatronics Engineering and IoT Education

An embedded Linux mobile robot was designed to support teaching in Multidisciplinary Engineering Technology (MXET) at Texas A&amp;M University. This robot is the Sensing, Connected Utility Transport Taxi for Level Environments (SCUTTLE). It is a payload-ready mobile platform made from off-the-shelf parts and 3D printed designs. SCUTTLE integrates a camera, ultrasonic sensor, encoders, optional LIDAR, and various sensors. SCUTTLE has been used in lab lessons since 2019 in the Mobile Robotics undergraduate course (MXET 300). The mechanical, hardware, and software source codes are open and available on GitHub to support mechatronics education.

Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

ABSTRACT In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection.

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

Research of Cartographer Graph Optimization Algorithm Based on Indoor Mobile Robot

In the recent years, mobile robot technology has received widespread attention from all over the world, and Simultaneous Localization and Mapping (SLAM) technology has always been a hot topic for researchers. In this paper, the Cartographer graph optimization SLAM algorithm is studied. Aiming at the problem that loopback detection may have wrong loopback points in the presence of noise, similar and uniform characteristics, a delay judgment module is added to filter the wrong loopback points. The Cartographer algorithm before and after optimization is deployed to the mobile robot platform based on ROS, and a 2D laser scanner is used for the map of indoor building. The experiment results prove that after the algorithm is improved, the overall relative error of the map is reduced by 1.148%, and the accuracy of mapping is improved.

The Use of Partial Stability in the Analysis of Interconnected Systems

Abstract In this paper, we develop sufficient conditions for uniform asymptotic stability of interconnected dynamical systems that are not in cascade form. We show that the stability analysis of a two-subsystem interconnection can be reduced to ensuring the stability of the first nonisolated subsystem with respect to its own state vector (partial stability) and the stability of the isolated second subsystem. In addition, based on the above results, we provide a control design framework for nonlinear systems where the control objective reduces to stabilization of only a part of the system state while guaranteeing the stability for the entire state of the system. We validate the efficacy of the proposed control framework via simulations and experiments using the wheeled mobile robot platform.

A Smart Robotic Walker With Intelligent Close-Proximity Interaction Capabilities for Elderly Mobility Safety.

The elderly population has rapidly increased in past years, bringing huge demands for elderly serving devices, especially for those with mobility impairment. Present assistant walkers designed for elderly users are primitive with limited user interactivity and intelligence. We propose a novel smart robotic walker that targets a convenient-to-use indoor walking aid for the elderly. The walker supports multiple modes of interactions through voice, gait or haptic touch, and allows intelligent control via learning-based methods to achieve mobility safety. Our design enables a flexible, initiative and reliable walker due to the following: (1) we take a hybrid approach by combining the conventional mobile robotic platform with the existing rollator design, to achieve a novel robotic system that fulfills expected functionalities; (2) our walker tracks users in front by detecting lower limb gait, while providing close-proximity walking safety support; (3) our walker can detect human intentions and predict emergency events, e.g., falling, by monitoring force pressure on a specially designed soft-robotic interface on the handle; (4) our walker performs reinforcement learning-based sound source localization to locate and navigate to the user based on his/her voice signals. Experiment results demonstrate the sturdy mechanical structure, the reliability of multiple novel interactions, and the efficiency of the intelligent control algorithms implemented. The demonstration video is available at: https://sites.google.com/view/smart-walker-hku.

Study of a robotic system to detect water leakage and fuel debris-System proposal and feasibility study of visual odometry providing intuitive bird\u2019s eye view-

To obtain the necessary information on fuel debris and water leakages during the decommissioning task of the Fukushima Daiichi Nuclear Power Plant, an ultrasonic-based method was proposed for future internal investigation of the primary containment vessel (PCV). In this article, we describe the rotatable winch mechanism and visual localization method, which were used to aid the investigation. We used the rotatable winch mechanism to adjust the height and orientation of the ultrasonic sensor and localized the robot with cameras to localize the sensor, to provide assisting information for data combination. We studied the feasibility of the conventional visual odometry method for application to the situation and performed localizing accuracy evaluation experiments with a mobile robotic platform prototype. The results showed that the visual odometry method could generate intuitive bird’s-eye-view maps, and provided an average error rate of 35 mm/1500 mm, which met the required maximum error rate of 100 mm/1500 mm for the grating movement. Experiments were also conducted with adjustable parameter ranges that could provide the required accuracy.

Exploration of Indoor Barrier-Free Plane Intelligent Lofting System Combining BIM and Multi-Sensors

Lofting is an essential part of construction projects and the high quality of lofting is the basis of efficient construction. However, the most common method of lofting currently which uses the total station in a multi-person cooperative way consumes much manpower and time. With the rapid development of remote sensing and robot technology, using robots instead of manpower can effectively solve this problem, but few scholars study this. How to effectively combine remote sensing and robots with lofting is a challenging problem. In this paper, we propose an intelligent lofting system for indoor barrier-free plane environment, and design a high-flexibility, low-cost autonomous mobile robot platform based on single chip microcomputer, Micro Electro Mechanical Systems-Inertial Measurement Unit (MEMS-IMU), wheel encoder, and magnetometer. The robot also combines Building Information Modeling (BIM) laser lofting instrument and WIFI communication technology to get its own position. To ensure the accuracy of localization, the kinematics model of Mecanum wheel robot is built, and Extended Kalman Filter (EKF) is also used to fuse multi-sensor data. It can be seen from the final experimental results that this system can significantly improve lofting efficiency and reduce manpower.

Development of an Autonomous Robotics Platform for Road Marks Painting Using Laser Simulator and Sensor Fusion Technique

SUMMARYThe design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.

Robotic Episodic Cognitive Learning Inspired by Hippocampal Spatial Cells

This letter presents a robotic episodic cognitive learning framework based on the biological cognitive mechanism of hippocampal spatial cells. By emphasizing the cognition process and episodic memory in brain, the framework adopts the velocity modulated grid cells and place cells to afford the robot position cognition, abstracts the state neurons to represent the robotic state, and uses state neurons' activity and connection to simulate the episodic memory construction process. The episodic memory is formed by a sequence of particular events consisting of visual features, state neuron, phase, and pose information. Besides an episodic-cognitive map building approach based on this framework is proposed, which performs closed-loop correction by resetting the spatial cells phase to keep the map accurate. The episodic-cognitive map built in this letter is a topological metric map to describe the topological relations of the particular events coordinates in the unknown environment. The framework is applied on a mobile robot platform, the robotic episodic cognitive learning and episodic-cognitive map building approach are investigated. The robotic experiments demonstrate that the framework can effectively achieve the robotic incremental accumulative learning, update the spatial cognition to the environment and construct the episodic-cognitive map.

Developing a real time navigation for the mobile robots at unknown environments

&lt;p&gt;&lt;span&gt;This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.&lt;/span&gt;&lt;/p&gt;

Development of Floor Mapping Mobile Robot Algorithm Using Enhanced Artificial Neuro-Based SLAM (ANBS)

A complex and expensive system in floor mapping mobile robot platforms are the challenges in this age of technology revolution. Sensors that are equipped with the robot could be different, the complexity of the algorithm and the robot performance itself are not adequate. In this paper, we present an efficient way with an economically cost-saving mobile robot floor mapping system based on simultaneous localization and mapping (SLAM). The paper will highlight implementing a Rplidar sensor with a floor mapping mobile robot platform with the enhanced error corrections based on the Artificial Neuro-Based SLAM (ANBS) algorithm. The proposed system runs on Robot Operating Systems (ROS) and Tensor Flow programming. The experimental results showed how the different controllers can be improved by adding the ANBS algorithm which intelligently filtering the unnecessary error and produce the precise output on the map. The different controllers also can be used with this algorithm. For this research, the ANBS are tested on Hector SLAM and Gmapping SLAM where the output produced by each SLAM method is fed into the ANBS algorithm. At the end of the experiment, the ANBS improves the output result by 14.67% for Hector SLAM and 17.36% for the Gmapping SLAM and produces a precise map than ever before. In the future, there will be more SLAM method can be embedded with this ANBS algorithm.

ON THE STABILITY OF MOBILE ROBOTS MOVEMENT WITH CABLE PROPULSION DEVICES

The movement of the platform of the underwater mobile robot with an excessive number of propulsion devices, clinging to the reservoir’s bottom, is considered. The task of determining the laws of the transition process, assessing its stability and quality indicators of control when establishing the specified relations between the efforts in the cables is studied.

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots.

This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.

A 3D Computer Vision-Guided Robotic Companion for Non-Contact Human Assistance and Rehabilitation.

With the rapid aging of the U.S. population, the mobility impairment is becoming a more and more challenging issue that affects a large number of individuals. The research presented in this paper aims at helping the mobility-challenged individuals with a novel robotic companion, which is a walker-type mobile robot capable of accompanying the human user and keeping user at the center for protection and possible power assistance. The robotic companion is equipped with a 3D computer vision system, which provides a unique capability of sensing the human-robot relative position/orientation without physical contact or the need for wearable sensors. As such, the robotic companion enables the user to walk freely with minimum disturbance to his/her normal gait, relieving the user from the physical and cognitive loads associated with the use of traditional assistive devices. For the development of the robotic companion, the authors designed and fabricated a low-cost, differentially steered mobile robotic platform, and also developed a unique image processing system to extract the position/orientation information from the 3D camera-captured images. Furthermore, an advanced motion control system was developed for the robotic companion, which provides novel solutions to the unique challenges such as sway reduction and noise reduction in digital differentiation. To quantify the performance, component and system-level experimentation was conducted, and the results demonstrated that robotic companion and its key components function as desired and the system is expected to reduce the user load and improve the user mobility in real-world scenarios.

An Autonomous Human Following Caddie Robot with High-Level Driving Functions

Nowadays, mobile robot platforms are utilized in various fields not only for transportation but also for other diverse services such as industrial, medical and, sports, etc. Mobile robots are also an emerging application as sports field robots, where they can help serve players or even play the games. In this paper, a novel caddie robot which can autonomously follow the golfer as well as provide useful information such as golf course navigation system and weather updates, is introduced. The locomotion of the caddie robot is designed with two modes: autonomous human following mode and manual driving mode. The transition between each mode can be achieved manually or by an algorithm based on the velocity, heading angle, and inclination of the ground surface. Moreover, the transition to manual mode is activated after a caddie robot has recognized the human intention input by hand. In addition, the advanced control algorithm along with a trajectory generator for the caddie robot are developed taking into consideration the locomotion modes. Experimental results show that the proposed strategies to drive various operating modes are efficient and the robot is verified to be utilized in the golf course.