Underwater robotics simulation with isaac sim
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The document discusses the development and simulation of underwater robotics, focusing on generating image data for teleoperated and autonomous systems. It highlights various challenges in underwater environments, such as light attenuation and visibility issues, and emphasizes the benefits of simulations for testing and refining algorithms efficiently. Integration of reinforcement learning and realistic simulations helps accelerate the advancement of underwater robotics in various applications, including inspection, mapping, and warfare.
Underwater robotics simulation with isaac sim
- 1. Underwater Robotics Simulation Generating Image data Creating a simulation testbed RL Training ground
- 2. A Slide on Neuronic Systems • Building Teleoperated & Autonomic systems • UAVs, Drones, Airborne Reconnaissance • Autonomous (Under)/Ground Vehicles • Maritime solution: Autonomous Surface Vessels • Guided Missiles / Low-Cost Missiles / Large Missiles • Highly optimized for Nvidia and Qualcomm platforms • End-to-End solutions from cameras & sensors to video and sensor stream processing, metadata and control
- 3. Motivation & Applications Growing need for ROVs/AUVs in underwater inspection, mapping, research, warfare 1 High cost & risk of real underwater testing 2 Simulation reduces development time, enhances safety, tests very rare events (“Legendary”) 3 Reinforcement learning accelerates autonomy development 4
- 4. Underwater Environmental Challenges • Light attenuation, reflection & scattering • Color shifts with depth and distance • Turbidity & particulate matter reduce visibility • Complex geometry in confined spaces (pools, tunnels)
- 5. Effects of Light & Color • Shorter wavelengths (blue/green) penetrate deeper than red • Objects appear more monochromatic or bluish with depth • Dynamic lighting conditions (sun angles, artificial lights)
- 6. Light & Color in ISAAC SIM & Physx • Physically Based Rendering (PBR) simulates wavelength-dependent attenuation • Adjustable parameters (turbidity, lighting) for realistic conditions • Ability to script environments for controlled experimentation
- 7. Neural Network Post-Processing • NN-based color correction & enhancement for synthetic images • GANs in the past or Transformer today improve fidelity to real underwater imagery • Better synthetic-to-real domain adaptation for perception algorithms
- 8. ROS Integration with ISAAC ROS: Standardized messaging, SLAM & navigation packages, quick prototyping ISAAC Sim: High-fidelity environment & sensor modeling, GPU- accelerated, Omniverse integration, Ray-Tracing for High fidelity Combined stack for rapid iteration and smooth sim-to-real transitions
- 9. Underwater Robot Sensor Suite Sensor Use in SLAM & Mapping Underwater Considerations Camera (RGB/Mono) Visual features Limited by turbidity & lighting shifts Depth Camera Dense mapping Reduced effective range underwater Sonar Acoustic structure mapping Reliable in low visibility, complex modeling needed DVL Velocity estimation Stabilizes drift in known-floor settings IMU Inertial data fusion Unaffected by visibility, needs correction Depth/Pressure Vertical position (Z-axis) Stable altitude reference
- 10. Underwater SLAM Challenges - OPTICAL DEGRADATION REDUCES RELIABLE VISUAL FEATURES - CONFINED SPACES (TUNNELS, POOLS) DEMAND ROBUST SENSOR FUSION - HEAVY RELIANCE ON ACOUSTIC SENSORS (SONAR, DVL) + IMU TO MAINTAIN STABLE POSE ESTIMATES
- 11. Sonar Simulation Approaches No native sonar “Gem” in ISAAC Sim Approximate via raycasts + custom signal processing Integrate external acoustic models via ROS for realism
- 12. ISAAC SIM for Robot & Sensor Simulation Import robot models (URDF/USD) into ISAAC Sim Import Simulate realistic hydrodynamics, thruster response Simulate Test closed-loop control with ROS-based navigation stacks Test
- 13. Real-Time Control & Navigation Testing • Validate path planning, obstacle avoidance • Train and refine SLAM algorithms with repetitive, consistent scenarios • Reinforcement learning accelerates training by leveraging synthetic environments
- 15. Benefits of a Virtual Testbed • Cost-effective and safe environment for early R&D • Repeatable conditions to refine algorithms • Seamless integration of complex sensor suites & novel SLAM approaches • GPU-accelerated RL for fast autonomy development
- 16. Future Directions Advanced scattering, bioluminescence modeling Better acoustic simulations integrated directly in ISAAC Sim Cloud-based simulation for collaboration & large- scale experiments Enhanced reinforcement learning frameworks for underwater robotics
- 17. Conclusion & Q&A Integrated ROS & ISAAC Sim accelerate underwater robot development Advanced lighting, acoustic modeling, and RL improve SLAM fidelity Ready for questions and further discussion
- 18. Tal Rotholz, Sales Director Tal@neuronicode.com +972-52-8925915 Yossi Cohen, CTO Yossi@neuronicode.com +972-54-5313092 www.neuronicode.com Thank You