User Story: SMAUT Technology

Driving Innovation with AI: How SMAUT Technology is Transforming Autonomous Mowing with AI-MATTERS

The Czech Node of AI-MATTERS presents a user story of SMAUT Technology, a company collaborating with the Czech Institute of Informatics, Robotics and Cybernetics at Czech Technical University in Prague (CIIRC CTU). Together, they are advancing autonomous mowing solutions by enhancing navigation, obstacle avoidance, and AI-driven route optimization, delivering safer, smarter, and more efficient operations.

Introduction

SMAUT Technology is a trusted partner in the development, production, and servicing of autonomous mowing solutions. The company specializes in advanced, fully autonomous industrial mowing robots designed for challenging environments such as solar parks, airports, and large industrial sites.
By combining cutting-edge hardware with intelligent software, SMAUT Technology enables operators to achieve higher productivity, reduce operational costs, and improve safety standards.

Thanks to their collaboration with AI-MATTERS, SMAUT Technology was able to enhance its autonomous mowing systems, improve safety and reliability, and develop AI algorithms that optimize the operation of their robotic fleet. This is their story.

Role of AI or Robotics in Company Operations

AI functionalities and algorithms are a key component of autonomous operation and provide the following capabilities:

• Navigation and route planning
  AI/ML algorithms process sensor data (LiDAR, cameras, GPS, radar) and learn to optimally plan routes even in environments with moving obstacles. They enable dynamic recalculation of the route depending on the current situation on site.
• Object and obstacle recognition
  Computer vision (deep learning) detects pedestrians, other vehicles, pallets, signs, or temporary obstacles, minimizing collision risk and increasing safety.
• Prediction of behavior of other traffic participants
  Machine learning helps anticipate the movement of people, other machines, or handling equipment, allowing smoother reactions and avoiding sudden stops.
• Operational optimization and fleet management
  AI analyzes data from the entire fleet and optimizes deployment—assigning tasks, minimizing downtime, and ensuring smooth material flow. This is crucial when deploying a full fleet of robotic machines.
• Predictive maintenance and diagnostics
  Algorithms monitor operational data (battery wear, vibrations, motor temperatures) and predict possible failures, enabling planned maintenance before breakdowns occur.

What Challenges led the SMAUT Company to Collaborate With AI-MATTERS?

The cooperation is structured into several major blocks, focusing on expert services in sensor solutions, automation of mapping processes, and functionalities within the SMAUT Autonomous system:

• Sensor systems
  Machine localization without GNSS availability
  Reliable driving even under coverage, e.g., under PV panels
  Navigation/object detection in environments such as tall grass
  Obstacle detection using a combination of different sensor types
• Mapping
  Commissioning of the machine within a given space
  Driving and mapping in case of unexpected obstacles
  Data recording and map generation, including modifications based on autonomously driven routes
• AI algorithms in the SMAUT Autonomous system
  The collaboration includes AI algorithms integrated into the machine’s SW architecture, the SMAUT Autonomous system, and the MySMAUT operational platform, as well as ensuring resilience of these algorithms and the entire system against cyberattacks.

The Solution

AI-MATTERS provided SMAUT Technology with the resources and expertise to overcome these challenges. Through access to RICAIP Testbed Prague facilities, prototyping, testing, and AI algorithm development, the company was able to:

• Train AI models for obstacle recognition, localization, and mapping.
• Implement machine control based on sensor data, including LiDAR, cameras, radar, and ultrasonic sensors.
• Conduct data recording, map generation, and route optimization.
• Integrate AI algorithms into the SMAUT Autonomous system and MySMAUT platform, ensuring resilience against cyberattacks.

“SMAUT approached us with the challenge to develop and validate several key areas – from sensors for machine localization and navigation to map generation and finally to obstacle and other object recognition. All this has been done using state-of-the-art AI algorithms Our main goal is to deliver a solution that is reliable, secure, and ready for deployment. New AI algorithms are tested in the near-real environment of RICAIP Testbed Prague thanks to the equipment we have already had. Therefore, the transition to the final machine is more straightforward and the evaluation in the real environment is faster,” adds Pavel Burget, Director of RICAIP Testbed Prague, CIIRC CTU

The Major Outcome From the Cooperation

The primary outcome is to achieve the stated technological goals by delivering the proposed solution in the form of a prototype or functional sample, ready (including the preparation of an experimental workplace) for deployment and necessary testing and experimentation:

• Machine control and obstacle avoidance based on localization and fused sensor data
• Loading the site model with all its features, along with machine properties and configurations
• Proof-of-concept (PoC) integration of functional algorithms and collection of new datasets generated by their deployment – testing integration of developed AI algorithms (into MySMAUT and machines, obstacle avoidance, machine self-control for higher route accuracy, route generation into the platform, model generation and refinement)
• Final integration of individual functional algorithms into the MySMAUT platform and evaluation of system resilience to cyberattacks
• Final deployment audit of the SPLUNK operational-security platform after integrating AI algorithms into implementation and operation

Joint Events and Outreach

Official Promotional Videos of SMAUT Technology: