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Advanced Autonomous Robotics for Defense R&D

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Delivering Mission-Ready Navigation in GPS-Denied Environments

40% Field-Testing Reduction
30% Deployment Acceleratio

Client Profile:

Our client is a premier government research organization operating under the Ministry of Defence, Government of India. Based in Bengaluru, this strategic R&D institution specializes in advanced robotics, autonomous systems, and underwater defense technologies. With a highly skilled workforce of 500+ scientists and engineers, it drives cutting-edge innovations for national security applications, including unmanned platforms, AI-enabled systems, and secure communication solutions.

Problem Statement The client faced critical gaps in deploying autonomous systems for defense applications:

  • Localization Failure: Inability to maintain accurate positioning indoors/outdoors without GPS signals, risking mission integrity.

  • Sensor Fusion Complexity: Unreliable

    real-time integration of LiDAR, IMU, and stereo camera data, causing navigation drift.

  • Software Capability Gap: Limited in-house expertise to develop scalable ROS2-based autonomy stacks for path planning and 3D mapping.

  • Software Capability Gap: Limited in-house expertise to develop scalable ROS2-based autonomy stacks for path planning and 3D mapping.

  • Accelerated Timelines: Stringent 12-month
    deadline to meet Ministry of Defence
    milestones amid resource constraints.

Our Solution: MicroGenesis delivered a modular autonomous navigation stack with end-to-end capabilities:

  • Core Technical Architecture

 

ROS2 Foxy Navigation Stack:

  • Unified framework
    integrating real-time
    localization, 3D
    mapping, path
    planning, and mission
    control nodes.
  • Seamless sensor
    fusion for LiDAR, IMU,
    stereo camera, and
    GPS

Validation Infrastructure:

  • Hardware-in-the-Loop
    Simulation (HILS): 
    Gazebo/ROS2
    framework emulating
    underwater conditions,
    enabling 80%
    pre-deployment
    validation

Precision Localization Engine:

  • LiDAR-Inertial SLAM: 
    Generated real-time
    3D maps with <5 cm
    drift and 12-DOF
    altitude data.
  • EKF-Based Fusion:
    Combined IMU, GPS,
    and LiDAR inputs for
    continuous drift
    compensation in
    GPS-denied zones

Accelerated Timelines:

  • Stringent 12-month
    deadline to meet
    Ministry of Defence
    milestones amid
    resource constraints.

Development Approach

Phase Activities Tools/Outputs
Development Custom ROS2 nodes for
SLAM, EKF, path planning
C++, Python,
Nav2 Stack
Testing HILS validation of
obstacle navigation
Gazebo, RViz,
Ubuntu 20.04
Deployment Docker packaging;
on-premises integration
Docker,
GitLab CI/CD
Knowledge Transfer SDD/ICD documentation;
on-site training
Technical manuals,
simulation reports

Business Impact:
The solution delivered mission-ready autonomy within 12 months, achieving quantifiable outcomes:

Performance Metrics

Run a test migration

KPI Result Operational Impact Localization Accuracy Testing Efficiency Deployment Speed Project Timeline
Compliance
<5 cm drift in GPS-denied
environments
Reliable navigation
in critical zones
80% validation via HILS
pre-deployment
40% reduction in
on-site testing time
Dockerized
configuration
30% faster
integration
On-time delivery within
12 months
Met MoD defense
milestones

Strategic Advantages
Future-Ready Architecture:

Modular design enables AI-based semantic mapping and swarm coordination upgrades.

Client Autonomy:

Comprehensive
documentation/training
empowered in-house
team ownership.

Tactical Demonstration:

Successfully validated
autonomous multi-floor
transitions with dynamic
obstacle avoidance.