Overview

This dataset provides insights into the adoption of robotics and AI-driven automation across various industries over several years. It includes metrics such as the total number of robots adopted, productivity gains, job displacement, cost savings, and training hours required for skill development due to automation. This data can help analyze the socio-economic impacts of robotics in manufacturing, healthcare, logistics, and other sectors. Researchers, policymakers, and business strategists can use this dataset to understand trends in industrial automation and its implications on the workforce and economy.

Introduction

Educational Robots Statistics: Educational robots are specialized devices employed in the educational field to engage students and facilitate learning. Especially in science, technology, engineering, and mathematics (STEM).

These robots possess the capability to be programmed, feature sensors, and are often mobile, allowing them to interact with their surroundings.

They are available in various forms, ranging from DIY robotic kits to pre-programmed and remotely controlled robots, serving as hands-on learning aids.

Educational robots find widespread use in STEM education, coding instruction, and problem-solving tasks. Delivering practical knowledge and preparing students for future careers in technology-related professions.

While they offer advantages such as improved learning and the development of critical skills. Challenges like cost, teacher training, and maintenance should be considered.

Introduction

Robot Statistics: Robotics has become a key catalyst for technological advancement, revolutionizing industries by boosting productivity, accuracy, and safety. The integration of artificial intelligence, machine learning, and machine vision has made robots more advanced, enabling them to make smarter decisions and automate intricate tasks.

These innovations are being adopted across diverse sectors such as manufacturing, healthcare, logistics, and consumer services, minimizing human involvement in repetitive or hazardous activities. The widespread adoption of robotics is further fueled by government policies, academic research, and private sector investments, all contributing to innovation and broadening the range of robotic applications.

This dynamic shift underscores the vital role robots play in enhancing operational efficiency and transforming industries, as they are increasingly utilized to streamline processes, improve safety, and address long-standing challenges.

In the bustling corridors of a Tokyo hospital, a robot named "Nami" quietly wheels itself through the hallways, delivering medication to patients. Meanwhile, halfway across the world, agricultural robots in California prune vines with uncanny precision, guided not by human hands but by machine learning algorithms. This isn't science fiction;...

The global market for autonomous mobile robots (AMR) was sized at about *** billion U.S. dollars in 2021. The market is expected to grow at a compound annual growth rate (CAGR) of around ** percent, reaching the size of over **** billion U.S. dollars by 2028.

The average cost of industrial robots worldwide declined steadily over the past decade, from about ****** U.S. dollars in 2010 to ****** U.S. dollars in 2017. According to a recent forecast, related costs are expected to decrease to ****** dollars by 2025.

This dataset includes information on the type of tasks being carried out by the robots (e.g., Welding, Painting, Assembly, Inspection), along with associated sensor data and performance metrics.

Key attributes include:

Robot_ID: Unique identifier for each robot in the system. Task_Type: Type of task the robot is performing, such as welding, painting, or assembly. Component_ID: Identifier for the component being worked on by the robot. Sensor_Type: Type of sensor used to monitor the robot’s environment (e.g., LIDAR, Camera, Thermal). Sensor_Data: Data gathered by the sensor, which can include information such as obstacle detection, temperature readings, or object recognition accuracy. Processing_Time (s): The amount of time the robot takes to complete a task. Accuracy (%): The accuracy of the robot in completing its task, represented as a percentage. Environmental_Status: Indicates whether the robot's operating environment is stable or unstable. Energy_Consumption (kWh): The amount of energy consumed by the robot while performing the task. Human_Intervention_Needed: Whether human intervention was required during the task. Obstacle_Detected: Indicates whether an obstacle was detected during the task. Defect_Detected: Indicates whether any defects were detected during the task. This dataset is ideal for analyzing the performance of robots in real-world, dynamic environments. It can be used to assess the efficiency, accuracy, and adaptability of robotic systems in manufacturing settings, while also providing insights into sensor performance, energy consumption, and the need for human oversight.

Introduction

Robot Statistics: Robotics has become the fundamental infrastructure of the present. The pursuit of efficiency, precision, and safety across global industries has strengthened the role of robots as essential operational tools.

Driven by converging advancements in AI, sensor technology, and high-speed communication, the capabilities of automated systems are expanding exponentially, moving beyond static factory floors into dynamic environments like surgical theaters, logistics networks, and homes.

The profound impact of this transformation can only be understood through hard data. I’d like to discuss robot statistics, offering readers a clear perspective on the scale, velocity, and economic power of the global automation trend.

We are looking at a market shift where every major economic sector is recalibrating its human-to-robot ratio, changing labor, and unlocking unprecedented levels of productivity. So, let’s get started.

General This dataset contains real-world measurement data for data-based modeling of a differential-drive robot. The dataset is especially tailored for data-based modeling using Extended Dynamic Mode Decomposition (EDMD) for control-affine systems. It contains predecessor and successor pose data of the wheeled mobile robot (i.e., its position in the plane of an inertial frame of reference as well as its orientation w.r.t. the x-axis) when constant control inputs are applied to the robot, which is done for two different realizations of the differential-drive robot. In the first realization, a desired constant translational and rotational velocity is sent to the robot (kinematic realization), while in the second realization, the robot's control actions are desired translational and rotational accelerations (second-order robot). A total of three different datasets are provided, two for the kinematic mobile robot and one for the second-order robot. The second, smaller dataset for the kinematic mobile robot shall indicate the data-efficiency of the EDMD approach. For each of the three datasets, three raw data files with predecessor pose data (X_i.dat) and three raw data files of successor pose data (Y_i.dat) are provided, where the number i from the set {0,1,2} corresponds to the predecessor and successor data and indicates the applied control basis u_i. In addition to zero control (i=0), the EDMD approach requires data for the differential-drive mobile robot for two linearly independent constant control vectors over a predefined sampling time. Further information about the chosen control bases and the sampling times can be found in the readme files associated with the dataset directories. Notably, the dataset for the second-order robot realization additionally contains approximative velocity data as well as the exact times at which the pose measurement of the external motion capture system has been received. This additional time information is provided to facilitate the smoothing of the velocity data. File Setup The following files and directories are provided. kinematic_dataset1 This directory contains raw data files containing the predecessor and successor pose data for the first sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.1 seconds. kinematic_dataset2 This directory contains raw data files containing the predecessor and successor pose data for the second sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.05 seconds. secondorder_dataset This directory contains raw data files containing the predecessor and successor pose data for the sampling of the second-order mobile robot. Each line consists of [x-position, y-position, orientation, v (translational velocity), omega (angular velocity)]. The chosen constant control vectors read u0=[0 m/^2s, 0 rad/s^2], u1=[0.2 m/s^2, 0 rad/s^2], and u2=[0 m/s^2, 0.5 rad/s^2] and the sampling time is 0.05s. Note that additional time instances of the measured data are provided in the respective first column. This might facilitate the necessary smoothing of the translational and angular velocities. ProcessVisualizeKinematic.m This is a minimal MATLAB file which can be used to process and visualize the recorded data for the kinematic mobile robot. Further information can be found in the comments of the file. ProcessVisualizeSecondorder.m This is a minimal MATLAB file which can be used to process and visualize the recorded data for the second-order mobile robot. Further information can be found in the comments of the file.

Artificial Intelligence Robots Market size was valued at $6.86 billion in 2022 and is predicted to reach $77.73 billion by 2030.

This data set is captured from a robot workcell that is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell. The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot). Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.

Introduction

Robotic Process Automation Statistics: RPA is a transformative technology that leverages robot software to automate rule-based tasks within digital systems. It operates by identifying repetitive tasks and developing software bots to execute them.

Seamlessly integrating these bots with existing software applications. RPA offers numerous benefits, including cost efficiency, accuracy, scalability, and enhanced productivity.

Its adoption is on the rise across industries, with the global RPA market poised for significant growth. This technology has the potential to revolutionize business operations.

By reducing costs, improving efficiency, and allowing human employees to focus on more strategic activities. Ultimately enhancing overall productivity and competitiveness.

Worldwide industrial robot shipments amounted to some ******* in 2022, just a slight increase compared to 2021. It is projected that industrial robot shipments will increase significantly in the coming years. It is expected that in 2026, global industrial robot shipments will amount to about *******. Leading industrial robot markets Japan, China, the United States, South Korea, and Germany are counted among the five leading industrial robot markets worldwide. In emerging manufacturing markets, the growth trend is largely driven by rising wages that make the use of machines appear a viable alternative to human labor. Leading applications for industrial robots Industrial robots can be deployed for a wide range of tasks in a growing number of industries. Although the highly automated car manufacturing sector remains one the largest areas of application for electro-mechanical machines, it was the electrical/electronics industry that installed the most industrial robots in 2020. It has to be noted that the field of robotics is a part of another industry: the automation market. This industry is comprised of a variety of products and services, including relays, switches, sensors and drives, machine vision and control systems, as well as industry software development and services. Conglomerates like Siemens, Mitsubishi Electric or General Electric are the major vendors of industrial automation and industry software. The key players in the industrial robot market include ABB, KUKA, Fanuc, Kawasaki, and Yaskawa.

This element involves the development of software that enables easier commanding of a wide range of NASA relevant robots through the Robot Application Programming Interface Delegate (RAPID) robot messaging system and infusing the developed software into flight projects.  In June and July of 2013, RAPID was tested on ISS as the robot messaging software for the Technology Demonstration Mission (TDM) Human Exploration Telerobotics (HET) Surface Telerobotics experiment.  RAPID has also been made available to — and integrated with — the Robot Operating System (ROS), a popular software framework for developing state-of-the-art robots for ground and space. While ROS powers a number of new robots and components such as Robonaut 2’s climbing legs and R5, the addition of RAPID allows these robots to interoperate in collaborative human-robot teams, safely and effectively over time-delayed communications links. The objective this year is to take this space-tested software and extend it to providing video streaming from remote robots and delivering this new capability to the Exploration Ground Data Systems (xGDS) area within HRS.  xGDS will then deliver its software to Science Mission Directorate (SMD) funded field tests to improve the technology readiness moving leading (potentially) to being used for the Lunar Prospector Mission ground data systems.  Success will involve delivering RAPID to xGDS and then xGDS supporting SMD field test.

The team is also developing algorithms for sensors capable of reconstructing remote worlds and efficiently shipping that remote environment back to earth using the RAPID robot messaging system.  This type of system could eventually lead to scientists on earth gain new insights as they are able to step into the remote world.  This sensor also has the ability to engage the public, bringing remote worlds back to earth.  During FY13, this task used science operations personnel from current SMD projects to objectively measure improvement in remote science target selection and decision-making based. The team continues to work with SMD projects to ensure that the technologies being developed are directly responsive to SMD project personnel needs. The objective of this work in FY14 is to expand the range of science operations tasks addressed by the technology, and to perform laboratory demonstrations for JPL/SMD stakeholders of the immersive visualization of data from a sensor using an SMD representative environment.

During 2014, the “Controlling Robots Over Time Delay” project element will develop two technologies:

• Develop RAPID robot messaging for unified cross-center operations platform for TDM, xGDS, and CCSDS
• Sensor Systems for the Construction of Immersive Virtual Environments

In 2024, the total number of industrial robots in the 'Volume Industrial robotics' segment of the robotics market worldwide was modeled to amount to *******. Between 2016 and 2024, the figure dropped by *******, though the decline followed an uneven course rather than a steady trajectory. From 2024 to 2029, the total number of industrial robots will rise by ******, showing an overall upward trend with periodic ups and downs.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Industrial Robotics.

Abstract

We provide a dataset gathered by teleoperating a wheeled robot robot platform over two types of surfaces: A) Smooth surface and B) Rough surface.

Content

The entire dataset has 276 files in total and has been organised into 2 folders, each for storing the data collected from one surface. outputs_2 folder contains data collected from a smooth surface and outputs folder includes data collected from a rough surface.

Each dataset file is a json file that stores a sequence data recorded from 1 episode of robotic control, i.e., data collected from robotic sensors and actuators while controlling the robot from an initial position at rest to the target position. The data collected at each time step of an episode contains the following records:

• num_records: no. of records in an episode
• direction: clock-wise/counter-clockwise depending upon the direction of movement of robot
• pose: current location/position of robot
• brake: 1 if the brake is applied, 0 otherwise
• angles: obtained from LiDAR. Ranges from -180 degrees to +180 degrees
• dists: obstacle distances obtained from LiDAR corresponding to respective LiDAR angles
• horn: 1 if pressed, otherwise 0
• counts_left: speed of the left wheel. Max speed = 2000 counts
• counts_right: speed of the right wheel. Max speed = 2000 counts

Experimentally it was found that approximately 800 counts correspond to 1 ft/s speed

A sample model for this dataset to predict the surface on which the robot is controlled has been provided for reference.

Note: This is a sample dataset. The detailed dataset will be published soon.

Artificial intelligence is reshaping robotics with tangible momentum. From guiding factory robots to powering humanoid helpers, AI is boosting efficiency and autonomy across sectors. For instance, UCL and Google DeepMind’s RoboBallet coordinates eight robot arms through 40 tasks, surpassing traditional systems by a wide margin. Meanwhile, Boston Dynamics’ Atlas now...

The intend of this data set is the cooperation within SimTech. It will be particularly interesting for data-based modeling and control which is a key area of the research of project network 4. We are proud to provide real-world data, which is essential for the benchmark of any data-based method. Additionally, we are able to provide reference solutions in order to evaluate the predictive quality of the methods tested. Finally, an example on how this data set can be used with Gaussian process (GP) regression in order to predict the systematic mismatches of the mobile robot is given. The data set contains input-output data of an omnidirectional mobile robot. The inputs to the mobile robots are the desired speeds in the plane as well as an angular velocity of the robot around its vertical axis. The corresponding outputs are the position in the plane and the robots orientation in an inertial frame of reference. The data set is provided in the Matlab *.mat format.

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the: - Development of robot health monitoring algorithms and tools - Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM) - Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation. For data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx). If you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov.

Snapshot img

Industrial Robotics Market Size 2025-2029

The industrial robotics market size is valued to increase USD 47.63 billion, at a CAGR of 19.4% from 2024 to 2029. Surge in demand for industrial robots will drive the industrial robotics market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 42% growth during the forecast period.
By Type - Articulated segment was valued at USD 8.68 billion in 2023
By End-user - Electrical and electronics segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 270.02 million
Market Future Opportunities: USD 47626.80 million
CAGR from 2024 to 2029 : 19.4%

Market Summary

The market represents a dynamic and continuously evolving landscape, driven by the integration of advanced technologies and the surging demand for automation in various industries. Core technologies, such as artificial intelligence (AI) and machine learning (ML), are revolutionizing robotics applications, leading to increased efficiency, flexibility, and precision. According to recent reports, The market share is projected to reach 65% adoption rate by 2025, driven by sectors like automotive, electronics, and food & beverage. Despite these opportunities, high costs associated with robotics services remain a significant challenge for market growth.
Regulations and standards, such as those set by organizations like the International Federation of Robotics (IFR), also play a crucial role in shaping the market landscape. The evolving nature of the market underscores its importance as a key driver of industrial innovation and productivity.

What will be the Size of the Industrial Robotics Market during the forecast period?

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How is the Industrial Robotics Market Segmented ?

The industrial robotics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Articulated
  SCARA
  Cylindrical
  Others


End-user

  Electrical and electronics
  Automotive
  Metal and machinery
  Pharmaceuticals
  Others


Product

  Traditional industrial robots
  Collaborative robots


Mobility Type

  Stationary robots
  Mobile robots


Product Type

  Materials handling
  Soldering and welding
  Assembling and disassembling
  Painting and dispensing
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The articulated segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth, with precision assembly robots and material handling robots leading the charge. According to recent reports, the market for precision assembly robots is projected to expand by 15%, driven by the increasing demand for automation in manufacturing processes. Similarly, the material handling segment is anticipated to grow by 18%, as businesses seek to streamline their operations and improve efficiency. Advancements in technology are also shaping the industrial robotics landscape. Three-dimensional sensor integration and robot vision systems are increasingly being used to enhance robot capabilities, enabling better error detection and improved accuracy. Welding robot applications are also benefiting from these advancements, with predictive maintenance models and force torque sensors improving productivity and reducing downtime.

Industrial robot controllers and machine vision integration are other key trends, with companies investing in advanced technologies to optimize robot performance and improve safety. Industrial automation systems are also becoming more sophisticated, with motion planning algorithms and collaborative robot safety features becoming standard. Robot cell design and dexterity are also critical factors, with six-axis robots offering the flexibility and versatility needed to handle a wide range of tasks. Payload capacity limits and articulated robot design continue to evolve, with delta robots offering faster speeds and lighter designs. The future of industrial robotics looks bright, with the market expected to grow by 12% in the next few years.

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The Articulated segment was valued at USD 8.68 billion in 2019 and showed a gradual increase during the forecast period.

The increasing adoption of robots in industries such as automotive, metals and machinery, and pharmaceuticals is driving this growth, as businesses seek to improve productivity, reduce costs, and enhance safety. In conclusion, the market is undergoing rapid transformatio