Collaborative Mobile Robot Research Articles

Stakeholder Perspectives on Safety Issues in Collaborative Mobile Robots: A Case Study of Quadruped Robot Applications in a Smart Factory

Stakeholder Perspectives on Safety Issues in Collaborative Mobile Robots: A Case Study of Quadruped Robot Applications in a Smart Factory

Methodology for Design of Hydrogen Robot for Medical Needs

The paper examines the concept of creating a Collaborative Service Robot powered by a Hydrogen Fuel Cell designed to assist in medical facilities. The characteristics of this robot are analyzed and its advantages are highlighted, from the point of view of ecological operation and long life during the patient service activities in a hospital facility. Some key features and benefits of Hydrogen Fuel Cell-powered robots are compared to battery-powered robots and the positives in this comparison are shown. The advantages of modern Collaborative Robots used as Service Robots in inpatient service processes in a hospital are presented. It shows the possibility, based on the competence and expertise of the authors in the field of Service Mobile Collaborative Robots, to create and implement one for the needs of human service in medical facilities, which will be powered by a hydrogen fuel cell. The main characteristics of the created mobile collaborative service robots of the ``AnRI'' series (Anthropomorphic Robot with Intelligence) are briefly indicated, where ``AnRI-1'' is equipped with a manipulator with a three-finger gripper, and ``AnRI-2'' is an information robot equipped with a large screen on top. The article analyzes the process of creating these collaborative robots, realizing several important elements of the control system, such as its development with elements of Artificial Intelligence. The advantages of hydrogen power and increased reliability of the general complex of systems in these Collaborative Mobile Robots and their possibility to be used effectively in the realization of medical care are emphasized.

Experimental analysis for comparison of wireless transmission technologies: Wi-Fi, Bluetooth, ZigBee and LoRa for mobile multi-robot in hostile sites

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-to-noise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments.

POMDP-based probabilistic decision making for path planning in wheeled mobile robot

Path Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot. To achieve robust behavior for mobile robots in the presence of static and dynamic obstacles, it is pertinent that the robot employs a path-finding mechanism that is based on the probabilistic perception of the uncertainty in various parameters governing its movement. Partially Observable Markov Decision Process (POMDP) is being used by many researchers as a proven methodology for handling uncertainty. The POMDP framework requires manually setting up the state transition matrix, the observation matrix, and the reward values. This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course. Selective test cases are run on the two robots with three categories – MDP (POMDP with belief state spread of 1), POMDP with distribution spread of belief state over ten observations, and distribution spread across two observations. Uncertainty in the sensor data is simulated with varying levels of up to 10 %. The results are compared and analyzed. It is demonstrated that when the observation probability spread is increased from 2 to 10, collision reduces from 34 % to 22 %, indicating that the system's robustness increases by 12 % with only a marginal increase of 3.4 % in the computational complexity.

A review of visual SLAM for robotics: evolution, properties, and future applications.

Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow.

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method.

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object's orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of Δφ = 0.51°, which corresponds to a positioning accuracy of ΔR = 6.55 cm, with a position update frequency of fupdate = 50 Hz.

Zonation Method for Efficient Training of Collaborative Multi-Agent Reinforcement Learning in Double Snake Game

This paper proposes a zonation method for training the two reinforcement learning agents. We demonstrate the method's effectiveness in the double snake game. The game consists of two snakes operating in a fully cooperative setting to maximize the score. The problem in this game can be related to real-world problems, namely, coordination in autonomous driving cars and the operation of collaborative mobile robots in warehouse applications. Here, we use a deep Q-network algorithm to train the two agents to play the double snake game collaboratively through a decentralized approach, where distinct state and reward functions are assigned to each agent. To improve training efficiency, we utilize the snake sensory data of the surrounding objects as the input state to reduce the neural network complexity. The obtained result show that the proposed approaches can be used to train collaborative multi-agent efficiently, especially in the limited computing resources and training time environment

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm.

Due to the increased employment of robots in modern society, path planning methods based on human-robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function's sub-functions and enhance the algorithm's performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique's ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot

Collaboration of multiple mobile robots becomes important in situations where collaborative tasks are required. For this purpose, a method including obstacle detection based on collaborative path planning of multiple Autonomous Mobile Robots (AMRs) has been developed. This study ensures the usability of collaborative omnidirectional AMRs technologies in various fields. In the study, novel path planning and obstacle avoidance algorithms are developed for collaborative mobile robots with omnidirectional mobility. Essentially, these algorithms include motion planning performed by obstacle avoidance with two identical 3-wheel omnidirectional mobile robots (TWOMR). Numerical calculations of the collaborative algorithm have been performed after the kinematic calculations, and tested algorithms developed for the proposed model. As a result, it has been observed that the path planning and obstacle avoidance algorithms developed for collaborative omnidirectional AMRs successfully follow the Master robot in the most efficient trajectory without collision and it has been observed that the developed method works with high accuracy.

Collaborative mobile robots: Bringing greater productivity and flexibility to warehouse operations

In an increasingly digital world, warehouses need to implement automation in order to reach optimal productivity and customer demand. Customer behaviour changes resulting from the COVID-19 pandemic have only heightened the need for automation, bringing record-breaking, peak-level demands to fulfilment operators at various points throughout 2020. This paper discusses the many benefits of implementing wall-to-wall fulfilment automation, including collaborative mobile robots, into warehouse operations. Most commonly users see greater efficiency, flexibility and workplace morale. The paper also explores a use case of how global security and aerospace company Lockheed Martin greatly improved efficiency in its warehouse and solved major material handling challenges by adding collaborative mobile robots into its fulfilment centre.

Learning-based object detection and localization for a mobile robot manipulator in SME production

Increasing research attention has been attracted to automatic production processes in small and medium-sized enterprises (SMEs) using collaborative robotic systems. In this work, we develop an object detection solution based on deep learning on 3D point clouds for a collaborative mobile robot manipulator to automate SME production. In this solution, a 3D point cloud technology is adopted to measure the shape and depth information of targeted objects in SME production, for instance, name tags production and plug-in charging. Deep learning is then employed to deal with the uncertainty in 3D detection, such as inconsistent light conditions and the irregular distribution and structural ambiguity of point clouds. A 2D camera is employed to calibrate the relative positions of the mobile manipulator to workstations. The mobile robot manipulator is equipped with cameras, an in-house developed adaptive gripper, and a learning-based computer vision system developed in this work. The principle and procedures of the proposed 3D object detection and 2D calibration are presented in detail. The automatic name tags production and plug-in charging experiments are conducted to validate the object detection, localization algorithms, and tools developed and employed in production cases using the mobile robot manipulator.

A Collaborative Robot for Tea Harvesting with Adjustable Autonomy

A collaborative mobile robot has been developed for tea plucking in narrow terrain tea gardens. The robot executes stable side-by-side motions and carries most of the load of the tea harvesting machine. The robot is controlled with adjustable autonomy so that the human can provide a supervisory role and adjust the optimal height and forward speed of the cutting tool. During tea plucking, the robot can autonomously detect the tree rows and avoid colliding with the trees. The experimental results show that the proposed collaborative robot can stably co-work with humans and significantly improve working efficiency and comfort.

Wireless communication based on Raspberry pi and Codesys for mobile robots using IoT technology

The industrial environment is going through exponential changes, due to the diversity of technological solutions that appear more and more frequently and the increase of productivity at increasing capacities. Due to this fact in the industrial area the number of devices, processing systems, collaborative robots, mobile robots and industrial equipment is increasing more and more. Consequently, there is a need for communication and connectivity of entities, common physical or virtual functioning and decision making, all of which are fundamental to the transition to the new concept of Industry 4.0. This study presents how wireless communication can be achieved in a mobile robotic platform that serves an industrial sector with other equipment in the production area, such as industrial equipment, collaborative industrial robots or other mobile robots. Also in this paper is presented how to create an HMI interface for the mobile platform that can be accessed from a touchscreen display mounted on the robot or from any mobile device connected to the internet.

Fast head detection in arbitrary poses using depth information

Purpose This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots. Design/methodology/approach The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector. Findings The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively. Research limitations/implications The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples. Practical implications The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption. Originality/value The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.

Development of an autonomous smart-parallel-parking robot for the prototype of collaborative robots

One of the main problems of the collaborative mobile robot application is to share the exact information of the robot itself and the surrounding area. Each robot needs to maintain its stability and positioning in order to achieve the target. As one of the samples of achieving the positioning task, a parallel parking problem was used in this paper. This paper used a car-like robot to do a parallel parking task. Front wheels were steered by using a connected joint and a servo motor. Meanwhile, each of the rear wheels was connected to a motor DC. Four ultrasonic sensors were used to find the distance between the robot and its surrounding (fixed in front, back, middle right, and middle of right - back side). The sensors connected to an Arduino Uno as the main microcontroller. The robot used a positioning algorithm based on the distance to nearest objects. The robot is designed only for parking on the right side of the car with an assumption there is no obstacle in the left side of the car. The experimental results confirmed that our system can solve the parallel parking problem. However, during the test, the output of the sensor was were affected by the noise from the environment. Another problem was the robot hardly to move straight because the rubber tires were not installed neatly. In the future works, the data output needs to filter and corrected and the servo degree needs to be initially corrected based on the chassis and tires angle.

Integration of a Skill-based Collaborative Mobile Robot in a Smart Cyber-physical Environment

The goal of this paper is to investigate the benefits of integrating collaborative robotic manipulators with autonomous mobile platforms for flexible part feeding processes in an Industry 4.0 production facility. The paper presents Little Helper 6 (LH6), consisting of a MiR100, UR5, a Robotiq 3-Finger Gripper and a task level software framework, called Skill Based System (SBS). The preliminary experiments performed with LH6, demonstrate that the capabilities of skill-based programming, 3D QR based calibration, part feeding, mapping and dynamic collision avoidance are successfully executed and strategies for further expansion of the operational capabilities of the system are discussed.

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an a...