Analysis of Localization Algorithms for ROS-Based Mobile Industrial Robots

Autonomous service robots are gaining popularity as they find application in different sectors such as logistics, hospitality, medical, space exploration and military. It is widely seen that autonomous service robots help increase efficiency, reduce cost and ensure safer operations. Automating material handling operations in highly dynamic indoor environments is risky and involves use of complex algorithms. However, continuously advancing technologies and machine programming tend to overcome these challenges. The three essential functions of an autonomous robot are to identify its instantaneous location in three-dimensional space, generate the environment map and decide an optimal path for locomotion. Simultaneous Localization and Mapping (SLAM) is a widely used algorithm for navigation in unfamiliar environments. This chapter presents a cost-effective autonomous ground robot for automating material handling, transportation and other repetitive tasks in dynamically changing surroundings. The robot is programmed to map the indoor environment and localize itself in the workspace with the help of SLAM and Adaptive Monte Carlo Localization (AMCL). In addition, the robot can manoeuvre through the indoor workspace by avoiding static and dynamic obstacles. This work employs the GMapping open-source package in the Robot Operating System (ROS) for performing SLAM with the aid of a laser range finder, wheel odometry and inertial data. The GMapping algorithm uses a probabilistic approach to construct a map and estimate the robot’s location in two-dimensional space. In addition, this chapter presents an autonomous navigation feature with the help of the AMCL method for localizing the robot within an uncertain environment and making the robot move to its goal using move base actions in ROS. The path planning of the mobile robot is classified into global and local plans. The global plan is responsible for planning the path from the start point to the endpoint. The local plan is responsible for planning the actual movement of the robot by avoiding any dynamic obstacles not mapped earlier. This work uses the Dijistra algorithm for the global and the dynamic window approach (DWA) algorithm for the local path planning. The optimization algorithm minimizes the error between the global and local paths. The simulation study of the proposed algorithm is performed in a virtual environment called Gazebo in ROS, which is nearly similar to a real-time indoor workspace. Further, a robot prototype is fabricated, and its performance is evaluated in an unknown environment. The robot prototype is equipped with brushed DC motors with optical encoder feedback for wheel odometry (ODOM), Light Detection and Ranging (LiDAR), and Inertial Measurement Unit (IMU) sensors controlled using a Jetson Nano. The simulation and experimental results showcase the robot’s successful two-dimensional localization and navigation in the factory environment. The current system has the potential for indoor assistance during material transfer and logistic applications. Further upgrades to the system can make it usable for Automated Storage and Retrieval System (ASRS) applications in warehouses/factories.

Multi-sensor fusion based wheeled robot research on indoor positioning method

Many teams participating in robotic competitions achieve localization using a 2D map plotted using adaptive Monte Carlo localization, a robot operating system (ROS) open-source software program. However, outdoor environments often include nonlevel terrain such as slopes. In the indoor environment of multilevel structures, the data representing different levels overlap on the map. These factors can lead to localization failures. To resolve this problem, we develop a software by combining HDL localization, which is an ROS open-source software, with our own program, and use it to achieve localization based on a 3D map. Furthermore, the authors observe the erroneous recognition of a slope as a forward obstacle during a competition event. To resolve this, we propose a method to correct erroneous recognition of obstacles using a 2D laser range finder and 3D map and confirm its validity in an experiment carried out on a slope on a university campus.

Aiming at the problems of slow positioning speed and information loss in the process of autonomous navigation of robots, we propose the adaptive Monte Carlo Localization (AMCL) algorithm based on lidar data fusion under the Robot Operating System (ROS) development system, realizing the robot for faster positioning and navigation.

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

The paper presents a framework for interactive control of multi robots using mixed reality interfaces. The proposed approach can operate with both single and multi-robot systems, including interaction with industrial manipulators, mobile robots, and unmanned aerial vehicles (UAV). The human–robot interface allows both interactive and physical interaction with the robot. Particular attention is paid to a visualisation of the robots' intent during control. The presented framework has Robotic Operation System (ROS) as a core that can be connected to any robot and mixed reality interface. This allows us to make framework scalable and connect several different mixed reality devices and multiple heterogeneous robots. In the implementation part we connected our framework with Microsoft HoloLens, collaborative robot KUKA IIWA, mobile platform Plato, UAV designed on the basis of dji f450. The proposed framework was tested experimentally with real experimental setups mentioned above. The results showed the capabilities of the mixed reality system for interactive control of different robots' type.

The increased usage of mobile robots in the industrial context entails higher safety risks for employees on the production floor. To enable safety, the usage of security concepts on industrial mobile robots is essential. One step towards security is authentication that is necessary to prevent unauthorized people from manipulating an industrial mobile robot’s software or configuration. Traditional authentication schemes that utilize username and password are not feasible for industrial mobile robots as either (a) a remote connection would be necessary to check the credentials or (b) the credentials need to be checked locally on the robot. Remote connections are problematic due to connectivity problems similar to them at RoboCup Logistics League competitions. If the credentials need to be checked on the robot, the usernames and passwords of all authorized people need to be stored and maintained there. As both possibilities are not feasible for industrial mobile robots, we propose an offline authentication approach that uses biometric data to authenticate a user on a mobile robot. The approach uses expiring passwords and a smart card to authenticate authorized people on the mobile robot. The smart card is equipped with a fingerprint reader to check that only authorized people are allowed to authenticate at a mobile robot. To show that the approach is able to provide secured authentication, a threat analysis is performed.

The days when humans and robots have not yet interacted in daily activities are gone. In fact, robots are on their way to changing their applications from industry to contributing to the well-being of people in everyday life. These robots are called "social robots" (SR). Unlike robots that simply describe what helps them, social robots aim to establish social interactions and improve the socialization of human beings. While many industrial robots are primarily used by manufacturing companies for hazardous or menial tasks, social robots have become a popular choice due to their quality and the positive impact they have on productivity and profitability. Industrial robots have anthropological features and are generally reprogrammable machines. Their mechanical arms are crucial components, and they possess other, albeit less prominent, features such as decision-making abilities, a range of emotions, the ability to respond to inputs, and communication skills. They are widely used in various industries, including material handling, assembly, and machines for applications like material handling, assembly, and machines for applications. These robots play an essential role in increasing efficiency and quality. Robots in manufacturing companies are highly valued for their ability to automate tasks and be reprogrammed for different functions. They possess various features optimized for specific handling tasks, including the ability to move in two or more axes and respond to different sensory inputs. This comprehensive set of features makes them suitable for a wide range of applications, including assembly, welding, material handling, loading, packaging, and inspection, where endurance, speed, and accuracy are required. The selection of industrial robots is a decision-making process based on the needs of production. Making the right decision is crucial for productivity and success. Choosing the wrong robot can lead to issues such as inefficiency or the inability to perform specific tasks within a multi-scheduled production. In the worst-case scenario, a completely unsuitable robot can render the entire company unusable. The complexity of the selection process is amplified by the diversity of robot manufacturers, as well as the significant variations in manufacturing jobs. Manufacturers themselves have recognized the importance of addressing these challenges, particularly in relation to the demands and intensity of specific tasks. Unity of ideal solution (TOPSIS). is prioritized by, this is a multi-criteria decision Analytical method. TOPSIS Abbreviation of (PIS). Select The short geometric distance alternative is positive The best solution is, basically The Great Solution of Thought (Nis) To be negative Distance is geometric. TOPSIS The assumption is even higher is, is coming or going The benchmarks are increasing. Scaling problems or Many in the criteria Parameters Mostly Improper Dimensions Due to normalization Generally required. Alternative taken as load capacity, Maximum tip speed, Memory capacity, Manipulator reach, Repeatability, Positioning accuracy. Evaluation parameter taken as Industrial robot 1, Industrial robot 2, Industrial robot 3, Industrial robot 4, Industrial robot 5, Industrial robot 6, Industrial robot 7. From the result it is seen that Industrial robot 3 is got the first rank where as is the Industrial robot 1 is having the lowest rank. Keywords: Industrial Robot, Manufacturing industry, Tools or Facilitator teachers.

This work refers to the realization of a Cyber Physical System (CPS), i.e. modeling, analysis, and integration of a MIR (Mobile Industrial Robot) platform with an industrial robot, in order to test its operating conditions. MIRs, although usually employed in industrial environment, can be used in sectors such as inspection, assistance, defense, production, remote exploration as well as search and rescue. In addition of being programmable, thanks to the provision of sensors and navigation systems, they can adapt in real time to various environmental contexts and purposes of use. The objective of the paper is to create a CPS of a MIR that can basically perform two fundamental actions. The first one is to carry out movements in predominantly external environments and therefore with various types of terrain. The second fundamental action is the pick and place operations, i.e. the robot must be able to carry out the manipulation of objects.

Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreover, robots are often battery-driven, meaning that even a small energy improvement can help reduce its energy footprint and increase its autonomy and user experience.In this paper, we study the Robot Operating System (ROS) ecosystem, the de-facto standard for developing and prototyping robotics software. We analyze 527 energy-related data points (including commits, pull-requests and issues on ROS-related repositories, ROS-related questions on StackOverflow, ROS Discourse, ROS Answers and the official ROS Wiki).Our results include a quantification of the interest of roboticists on software energy efficiency, 10 recurrent causes and 14 solutions of energy-related issues, and their implied trade-offs with respect to other quality attributes. Those contributions support roboticists and researchers towards having energy-efficient software in future robotics projects.

Industrial robots are required to interact with the surrounding environment to perform a given task (e.g., an assembly task). However, standard industrial robots are commonly position-controlled. Therefore, there is the need to implement an outer compliance controller to guarantee a safe interaction. Such compliance controllers require force/torque measurements to close the loop, and most of the industrial manipulators available on the market do not have embedded force/torque sensor(s), requiring additional efforts (i.e., additional costs and implementation resources) for such integration in the robotic setup. To provide a standard industrial sensorless position-controlled robot with the capabilities to execute an interaction task, the proposed paper defines an external joint torques observer for the implementation of an outer sensorless compliance controller. More in detail, exploiting the resulting position-controlled robot dynamics, an Extended Kalman Filter (EKF) is proposed to estimate the external joint torques. Exploiting such an estimation, an outer impedance controller can be designed, providing a position/velocity reference to the inner position controller. The described approach has been validated with experiments on a Franka EMIKA panda robot. A human operator interacts with the controlled robot, applying external wrenches (i.e., both Cartesian forces and torques). The resulting external joint torques are estimated making use of the proposed EKF, comparing the achieved results with the signals provided by the joint torque sensors of the Franka EMIKA panda robot (measurements used as a baseline for validation purposes). Experimental results show the capabilities of the proposed control framework in estimating the applied external joint torques while implementing a position control-based compliance controller.

In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved.

The paper introduces a solution of Simultaneous Localization and Mapping what use basic sensors of TurtleBotlI like integrated odometer and gyroscope in Kobuki robot base, as well as XBOX 360 Kinect RGBD data. We can define new position and orientation of robot using previous location and sensors' data with Extended Kalman Filter (EKF) in indoor environment. The client program communicates with Robot Operating System with remote access in C#. Our results built into a higher system which called Teki Control Project.

The paper focuses on the challenges that the material handling on offshore petroleum installations represent, with regard to the possible harmful effects on health. The term material handling covers all handling of materials, goods and supplies. This includes offloading from a supply vessel and all operations in connection with operation, maintenance and repair. The term ergonomics covers all the manual handling in connection with the material handling operations. In Norway the term working environment (WE) includes ergonomics and other physical and mental factors that influence on the personnel. The effect of poor solutions for material handling may be low efficiency, exposure to risk, near misses, incidents and lost time injuries. There is a common goal from an operational and an HSE/ergonomic point of view to create solutions that make material handling efficient with a reduced risk for incidents, accidents and occupational diseases. In project work we are focusing on how we in a systematic manner can transfer our experience from operations to other installations and to modification or development projects. A systematic approach to ergonomics at an early stage in design creates better solutions and reduce the need for later modifications and corrective actions.

This article presents the design and implementation of an autonomous navigation framework for a Quadrotor-Unmanned Aerial Vehicle (UAV) based on Simultaneous Localization and Mapping (SLAM). The proposed system leverages the Extended Kalman Filter (EKF)-SLAM algorithm, operating solely on LiDAR data and without reliance on GPS, making it highly suitable for navigation in GPS-denied or cluttered environments such as indoor settings, tunnels, and urban canyons. A dynamic model of the UAV is developed to describe its motion under 3-DoF, forming the basis for both simulation and control. The EKF-SLAM algorithm is implemented within the Robot Operating System (ROS) and tested using the Gazebo simulation environment. Experimental results confirm the algorithm’s effectiveness in real-time localization and 3D environment mapping using LiDAR data, even in the absence proprioceptive input sensor.