Market Research Future's latest report indicates a projected expansion of the robotics market from $50.96 billion in 2022 to $214.60 billion by the end of this decade. The widespread adoption of robotics across various industries, including manufacturing, logistics, and healthcare, drives this surge. Still, the global robotics market is poised for remarkable growth until it addresses its most valuable by-product: data.
In this context, robotics data plays a central role in organizations enhancing their products and operations: Self-driving car manufacturers use data to bolster the safety and reliability of their autonomous vehicles, rapidly identifying and rectifying any issues. Likewise, warehouse automation firms optimize their operations using data to enhance robot efficiency; in manufacturing, data-driven insights improve product quality. Moreover, in recent years, deep learning and machine learning workflows have harnessed off-the-shelf analytics tools, spurring higher demand for quality data.
The adoption of robotics increases alongside the volume of data it generates. Former Intel CEO Brian Krzanich noticed this phenomenon, estimating that every day a single autonomous vehicle produces four terabytes of data, while each smart factory generates one petabyte. Hence, robotics enterprises face two pivotal challenges: the ingestion, storage, analysis, and extraction of metrics from vast data volumes originating from diverse sources; and leveraging quality data to feed machine and deep learning workflows.
Model-Prime’s goal is to meet those data challenges head-on and redefine the landscape of data workflows in the industry. Model-Prime provides a centralized cloud platform for managing robot logs, facilitating data extraction, metadata enrichment, and integration into existing pipelines.
Model-Prime assumes an integral role throughout the entire data lifecycle — from metadata extraction to log searches, model building, and validation processes. To this end, Model-Prime boasts three pillar features, each contributing to its remarkable efficacy:
- Rapid ingest: With Model-Prime, your logs are searchable within minutes of offloading, drastically decreasing waiting periods.
- Fast and responsive search: Built for concurrent access by your team members and processes. Model-Prime ensures swift data access, making complex file or SQL queries and their associated waiting times obsolete.
- Managed metadata: Search native log properties, enrich your logs with additional metadata, and select precise time ranges for tagging — all without the need for cumbersome spreadsheets. Model-Prime also supports ontology enforcement for customers that have very precise meta data needs.
This white paper makes the case that robotics companies must prioritize data management and analytics to usher in the next wave of innovation, making Model-Prime the right data platform for transforming their operations, increasing their development velocity, and maintaining a competitive edge. To this end, the following sections explore these data challenges and solutions in greater detail.
The Challenge: Managing and analyzing robotics data to improve operations
Robotics companies grapple with the challenge of efficiently managing and analyzing their data to enhance their operations and development processes. Traditionally, the robotics workflow involved pushing code to robots and manually observing the outcomes through logs. However, this approach can’t keep up with growing companies, who, at scale, have to handle data differently.
As the workforce of robotics companies expands, welcoming data-centric professionals such as analysts, developers, and data scientists, a fundamental transformation occurs. Simultaneously, the proliferation of increasingly sophisticated machines and machine systems generates an avalanche of data. Furthermore, the need for data extends across multiple use cases, making the inability to harness it an untenable situation. The distinct data challenges encountered by robotics companies include:
The Data Deluge Drowning Robotics Companies
Robotics companies are scrambling to stay afloat amidst an unrelenting deluge of data. More sophisticated robotics systems generate massive volumes of log data. This data, captured by different sensors and actuators, presents itself in diverse formats and sources. The scale and complexity of ingesting this flood of heterogeneous data can delay data accessibility and consequently hamper both business and development operations.
Cracking the code of complex, unstructured log data
Once ingested, making sense of robot logs proves arduous. Due to their heterogeneous nature, it’s difficult to automate the direct extraction of useful metrics, like robot performance indicators. Yet, manually inspecting dense log files second by second simply does not scale. Consequently, analytical tools and workflows to efficiently bridge raw log data with downstream analytics and insights have become necessary.
The Metadata Conundrum
Enriching robot logs with metadata enables advanced workflows, from analysis tasks to the identification of data needed to improve model training and validation. Since this data exists in complex, diverse time-series formats across multiple channels, manually appending metadata is tedious and error-prone. The process can appear daunting, given the absence of tools and workflows to bridge the gap between raw data and its application in downstream processes, chiefly resimulation, machine and deep learning, and triage and performance troubleshooting. Consequently, existing solutions seek to develop ways to automate the process of contextualizing vast volumes of heterogeneous robot data.
Integrating Data into ML/DL Pipelines
AI-powered robotics workflows are on the rise and promise to revolutionize the capabilities of robots across industries. But to train and iterate on machine learning and deep learning models, raw log data must be preprocessed into labeled datasets. The lack of seamless data preparation and integration with ML/DL pipelines stifles the development of intelligent robotics applications.
Building custom data applications and data-driven processes
Robotics companies often require specialized data applications and pipelines for unique needs like incident triage or pattern discovery to enable predictive maintenance. To this end, robotics companies need agile and customizable data platforms that fuel the development of purpose-built applications and processes.
The Solution: Model-Prime powers the next wave of robotics innovation
Model-Prime provides the platform for a new paradigm of robotics data workflows. It facilitates every stage of the data lifecycle, from collecting log information to searching the logs to building models and data retention.
1. Conditional search and text queries to easily surface patterns
One of the longstanding challenges in the robotics industry has been efficient log search. Model-Prime runs on the premise that searching your logs should be as straightforward as performing an internet search. The conventional method of managing log metadata in spreadsheets has proven insufficient at scale. Moreover, searching raw robotics log data with present-day solutions is inefficient at scale.
Model-Prime offers the ability to search by any recorded or appended log property through multiple modalities:
- Normal text-based querying: The standard method for searching logs, albeit limited in its capabilities. Model-Prime supports exact and fuzzy searching of log metadata.
- Conditional search: Search for events based on quantitative values and logical operators.
2. Enriching Data with Contextual Metadata
The Model-Prime platform facilitates the addition of metadata through our intuitive user interface and powerful API. This metadata enhancement process includes the application of tags, which can be categorized as:
- Tags applying to the whole log
- Tags of all channels for a defined timeframe
- Tags of specific channels within a log for a specified duration
With Model-Prime, you can employ these tags to spotlight areas of interest within your log data. Processes you might use for generating tags include:
- Machine learning ground-truth labeling: The process of manually or automatically assigning accurate labels to data for training machine learning models.
- Triage annotation: The systematic categorization or prioritization of data or tasks based on predefined criteria for efficient handling.
- Autotaggers: Model-Prime’s platform provides tooling to use rules you define to identify interesting events and tag data accordingly.
Enriching your robot logs with metadata provides you with the ability to:
- Search your logs across numerous attributes
- Effortlessly group similar logs
- Supply relevant log data to machine learning or other workflows
3. Getting logs into Machine Learning and Deep Learning Pipelines
Another aspect of Model-Prime’s workflow is the seamless integration of logs into your machine learning and deep learning pipelines. Model-Prime facilitates this process through a feature known as "Robosets." Robosets enable you to group logs (or "robologs") and snippets of robologs, streamlining a variety of operations:
- Log collection and image extraction
- Grouping relevant datasets for model training
- Bulk operations on logs
- Pipeline preparations for extraction and machine learning
4. Building custom data applications and processes
Our robust API and comprehensive documentation empower you to construct custom data applications tailored to your unique use cases. Examples of custom data applications include:
- Triage applications: Supporting custom triage workflows that evaluate robot performance and task priority.
- Industry-specific searching methods: Tailored to the nuances of your industry.
- Data flow to other applications: Facilitating data exchange between Model-Prime and first or third-party applications.
5. Unlocking data analysis at scale
Data within logs is encapsulated in the form of messages. Model-Prime’s proprietary pipeline allows you to select and funnel specific data channels into the data lake. From there, data analysis becomes accessible using SQL or Python, and you can create visualizations using tools like Jupyter Notebook and use queries for analysis opportunities for different objectives, such as:
- Optimizing maintenance and troubleshootingsome text
- Trending error rates by location or model
- Correlating motor faults with actions
- Guiding engineering and product prioritiessome text
- Visualizing robot usage by their capability
- Comparing software versions by reliability metrics
- Supporting strategic investment decisionssome text
- Mapping feature usage to ROI
- Segmenting cycle times by process type
Example workflow with Model-Prime
The following workflow illustrates how Model-Prime participates across the data lifecycle and enables companies to unlock more value from their existing robotics systems.
One of our clients is an leader in farming automation. As the company rapidly scaled up its fleet of robotics systems, it struggled with a massive influx of heterogeneous data from sensors and logs. Adopting Model-Prime allowed Atlas to overcome data hurdles in its operations and explore new technologies.
Data deluge
They were flooded with huge volumes of robot sensor logs in different formats from various warehouse locations. With Model-Prime's scalable cloud infrastructure, the company seamlessly ingested and consolidated data from all sources into a central repository.
Unstructured data
They had logs contain complex unstructured messages using Model-Prime's Conditional search and text queries to find patterns and determine performance metrics.
Lack of context
Logs lacked IDs and other metadata needed for analysis. The agtech firm took advantage of Model-Prime's intuitive interface and APIs to rapidly enrich logs with contextual metadata. Autotaggers, for their part, simplified adding metadata at scale.
ML/DL integration
To develop predictive maintenance capabilities, they needed to train ML models. Thanks to Model-Prime's "Robosets", Atlas’ ML engineers could group, label, and pipe log data into their ML systems.
Custom applications
Likewise, Atlas required a performance dashboard app to monitor robots’ performance over time. Model-Prime's APIs helped it to rapidly develop a customized app without starting from scratch.
Data analysis
Atlas connected Model-Prime's data warehouse to its BI tool. Running SQL queries and generating visualizations helped Atlas uncover usage insights for optimizing warehouse operations. As a result, Atlas cost-effectively transformed its data challenges into impactful, actionable insights.
The bottom line
If you are a robotics company looking for a way to manage and analyze your data more effectively, try Model-Prime today. To learn more about Model-Prime and how it can help your robotics company, please visit our website or contact us for a demo.
Sources
- Deloitte United States. “Lights-Out Processes in Warehousing.” Accessed October 23, 2023. https://www2.deloitte.com/us/en/blog/business-operations-room-blog/2023/lights-out-warehouse-automation.html
- Hamdan, Allam Mohammed Mousa, ed. Future of Organizations and Work after the 4th Industrial Revolution: The Role of Artificial Intelligence, Big Data, Automation, and Robotics. Studies in Computational Intelligence, volume 1037. Cham: Springer, 2022.
- https://www.marketresearchfuture.com, Market Research Future. “Robotics Market Report 2023, Global Size, and Share 2030.” Accessed October 23, 2023. https://www.marketresearchfuture.com/reports/robotics-market-4732
- Miller, Rich. “Data Center First: Intel’s Vision For A Data-Driven World.” Data Center Frontier, February 2017. https://www.datacenterfrontier.com/cloud/article/11430910/data-center-first-intel8217s-vision-for-a-data-driven-world
- Schume, Philipp. “Improve Product Quality and Yield with Intelligent, Secure, and Adaptable Manufacturing Operations.” IBM Blog (blog), April 17, 2020. https://www.ibm.com/blog/iot-manufacturing-ready/.
- “The Future of Autonomous Vehicles (AV) | McKinsey.” Accessed October 23, 2023. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/autonomous-drivings-future-convenient-and-connected
- “Unlocking the Industrial Potential of Robotics and Automation | McKinsey.” Accessed October 23, 2023. https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/unlocking-the-industrial-potential-of-robotics-and-automation
