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Assessment of AI and robotic solutions for testing

National Institute for Research in Digital Science and Technology  (INRIA)

Josephinum Research (JR)

This service includes all essential actions to gather knowledge, design, and document the client’s needs regarding Testing and Experimentation Facility (TEF) services. Through close collaboration with the client, we ensure that all technical, operational, and methodological aspects are clearly outlined, forming a solid foundation for future testing phases. By conducting these preparatory activities, we deliver a comprehensive setup analysis that serves as the entry point for subsequent testing and validation services and provision of datasets. This ensures that testing can be carried out efficiently, aligning with the client’s specific requirements while leveraging TEF’s expertise and infrastructure. Ultimately, this service streamlines the testing process, reduces uncertainties, and enhances the overall efficiency of AI and robotic solution validation.

Test design

Test setup

Benchmarking & Testing Suite for Edge Hardware Systems

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

The Benchmarking & Testing Suite for Edge Hardware Systems delivers a set of tests designed to evaluate the performance, reliability, and functionality of edge hardware and its components under various operational conditions.

The tests can be compared to specific industry standards or the performance of other solutions available on the market. Examples of tests offered as part of the service:

- Environmental tests: Assessing devices' resistance to extreme conditions, such as temperature, humidity, and vibrations.

- Signal tests: Evaluating devices using GNSS signal generators, testing their resilience to interference or false signals.

- Network tests: Evaluate device performance within a prototype 5G network infrastructure.

- Functional tests: Assessing the capability of devices, such as remote PTZ (pan, tilt, zoom) cameras, to perform operational tasks in field conditions.

- Integration tests: Examining the cooperation of edge devices with sensors, AI systems, and their responses to data input failures. - Accuracy tests: Measuring the precision of sensors and control systems. Test results can be compared to specified standards or the performance of competitive solutions, enabling customers to better understand their devices' capabilities.

Collection of test data

Desk assessment

Performance evaluation

Test design

Test execution

Test setup

Curation and provision of annotated datasets

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

The service provides access to annotated data collected from devices tested as part of other project services. It includes both data annotation services (manual and automatic) and the provision of annotated databases or data packages. In addition, we offer disc space for temporary archiving of the collected data along with its annotation in cases where this is not possible at the client's site. As a result of the service, procedures for both manual and automatic annotation will be identified that are appropriate to the type of measurement data and the way it is collected and assembled. The methods of labelling and assigning specific data classes collected by the agriFoodTEF technical infrastructure will be proposed. Possibilities for documenting and storing data in datasets will be indicated. In addition, procedures for extracting selected features from stored research data can be offered.

Collection of test data

Provision of datasets

Experimentation with synthetic data generation and data augmentation techniques

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

This service involves expanding training datasets using synthetic data and data augmentation techniques. This will use algorithms capable of generating synthetic data simulating certain aspects of existing datasets. Such an approach will lead to the enrichment of the existing training material. The service includes the creation of synthetic replicas based on real data, the combining of real and synthetic data, and also, for example, the creation of digital terrain maps based on ground data, which can be further enriched with synthetically generated details. As a result, we offer a comprehensive solution for more reliable development of datasets.

Collection of test data

Data analysis

Data augmentation

Provision of datasets

Generation and provision of labelled datasets for benchmarking purposes

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

The service provides the ability to deliver to the client model training data sets with labels and metadata, which will later be used to conduct comparative analyses of AI algorithms by the target groups using the technologies under study. The datasets delivered to the client will be configured or customised in advance, as appropriate, according to the requirements and needs of the client of the service. Customisation processes will include configuring and adjusting the data and labelling according to the scopes of field operations, technological processes or communications to which the transferred data assets relate. These data sets include, but are not limited to, satellite and spatial data for remote sensing, phenological data for different crops, or synthetic data to expand training opportunities.

Collection of test data

Provision of datasets

Providing demonstration testing on the test field of robotic or AI-driven solutions

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

As part of the service, a testbed will be provided for product demonstration of robotic machines along with AI systems. The polygon will be configured and prepared to meet the customer's individual needs in terms of field operations, technological processes, and communication. The service provider will provide polygons with a variety of surfaces according to the customer's specifications, e.g., meadow, ploughed field. The service will include soil moisture control, continuous monitoring of the field, and all activities carried out on it. The service providers will provide the necessary assistance and infrastructure needed to support the fieldwork carried out by the surveyed sites. The service may also involve demonstration of AI systems used in apiaries.

Data analysis

Desk assessment

People training

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Provision of cloud-based data storage

Lukasiewicz Poznanski Instytut Technologiczny

Poznan Supercomputing and Networking Center (PSNC)

Within the service we provide: - remote access to the disc space, allowing for safe storage of data (e.g., from sensors, photos from specialised cameras, weather data, satellite data, archive historical crop yields, etc.). - Depending on the needs, the available disc space can range from several gigabytes to tens, if not hundreds, of terabytes. - The stored data may be used directly by the customer or may be used in other services (this refers to using data for AI/ML model training, testing and validation for applications such as precision farming, yield prediction and pest detection). - The ability to store both structured (e.g., CSV, SQL) and unstructured data (e.g., images, videos)

Data analysis

Provision of general-purpose datasets with user-specified sensor(s)

National Institute for Research in Digital Science and Technology  (INRIA)

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this includes classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and supporting decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial and/or ground robots equipped with user-specified sensors, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Provision of general-purpose datasets via multisensory ground robot

National Institute for Research in Digital Science and Technology  (INRIA)

General-purpose datasets serve two primary objectives: (i) evaluating mobility algorithms and (ii) developing and assessing general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, SLAM (Simultaneous Localisation and Mapping), and navigation. Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks such as, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas like arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for monitoring these changes effectively. This real-time data collection, often facilitated by ground robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Provision of general-purpose datasets via a multisensory aerial robot.

National Institute for Research in Digital Science and Technology  (INRIA)

We provide general-purpose datasets that can be used by customers to evaluate mobility algorithms and to develop and assess general-purpose AI applications. In the context of mobility algorithms, this pertains to classical robotics tasks such as mapping, localisation, and SLAM (Simultaneous Localisation and Mapping). Meanwhile, general-purpose AI applications focus on advancing algorithms and feeding decision support systems (DSS) for tasks including, but not limited to, weed detection, health monitoring, growth and maturity assessment, and yield estimation in areas such as arable farming, horticulture, food processing, forestry, and tree management. A significant challenge in developing AI solutions for agricultural robotics lies in the dynamic nature of agricultural environments, which fluctuate with different seasons and weather conditions. To address this, acquiring consistent and periodic data is essential for effectively monitoring these changes. This real-time data collection, often facilitated by aerial robots, is crucial for developing efficient algorithms and AI solutions. Such datasets can support customers in the development of sensor-specific techniques or be leveraged to create multisensory algorithms, enabling more accurate and adaptable systems for agricultural applications.

Data analysis

Data augmentation

Desk assessment

Provision of datasets

Testbed preparation

Lukasiewicz Poznanski Instytut Technologiczny

Testbed preparation will enable costumers to execute physical tests of their technology/machines/algorithms in line with their expectations.

Within this service, costumers might get a well prepared flat/slopped arable lands or permanent grasslands.

We can also prepare a land with "controlled rain conditions" or a dump ground. There is also a possibility of providing and preparation of apiary or a sprinkler for software testing. We can help costumers run physical experiments by preparing a cultivated/non-cultivated fields or run tests on test plots, specially designed for testing. Within the latter, we will take care of such elements like: soil preparation, seed/plant procurement, seeding/planting, plant growth management, preparation of technical infrastructure. All this to meet your needs to ensure optimal conditions for conducting and monitoring tests.

This service can be executed in fields with various area with system of weather stations, phenological stations and ICT infrastructure. We can also prepare costumers sets of data collected for these areas like archive cultivated plants, treatments, crops, etc.

Testing and evaluating sensor devices

National Institute for Research in Digital Science and Technology  (INRIA)

The SOPHIA infrastructure will offer the possibility to test and evaluate the performance of a sensor device in a locally controlled environment. While our indoor facility provides a fully controlled environment (light, volume), all facilities in SOPHIA can be customised to test and evaluate the sensor device. The service consists of three main steps. First, the sensor device will be integrated into the facility. After that, the facility will be customised according to the final application of the sensor device. Then, the performance of the sensor device will be evaluated using quantitative and qualitative metrics. Comparison could be proposed as a complementary option to position the performance of the sensor device in relation to the current state of the technology available in SOPHIA.

Collection of test data

Data analysis

Desk assessment

People training

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup