Back to Solutions
Simulation, Modelling & Control
Advanced simulation platforms, system modeling, and control algorithms for autonomous systems optimization.
Featured Projects
Discrete Event Simulation (DES) for Robotics
Built a high-speed Discrete Event Simulation engine for multi-robot systems, delivering results in seconds instead of hours and enabling rapid design space exploration, What-If scenario analysis, and Monte Carlo-driven reliability estimation.
Challenges
- Traditional time-based robotics simulators took prohibitively long to run, making iterative system design and layout exploration impractical
- Evaluating different design configurations (fleet size, facility layout, task allocation) required running full physics-based simulations each time
- Computing reliability metrics like p95 or p99 performance required thousands of simulation runs, infeasible with existing tools
- No fast way to perform What-If scenario analysis for varying operational configurations across logistics, warehousing, and industrial automation domains
Solutions
- Developed a Discrete Event Simulation (DES) engine that models queues, resources, and decisions instead of continuous physical time
- Built automated layout parser converting facility designs to graph representations for simulation
- Enabled Monte Carlo simulations for design space exploration and p95/p99 reliability calculations
- Implemented visualization engine for simulation results and robot path analysis
- Designed a fully configurable system supporting robot parameters, layout parameters, and operational constraints
Outcomes
- Over 3,600x speedup — simulations complete in seconds vs hours with traditional time-based tools
- Less than 3% error compared to time-based simulation benchmarks, validated across multiple configurations
- Enabled rapid evaluation of fleet sizing and layout alternatives that were previously infeasible to explore
- Unlocked Monte Carlo-driven design space exploration for robust system design
- Paved the way for Multi-Agent Path Finding (MAPF), RL-based layout optimization, and online forward simulation
Physics-Informed Neural Networks for Satellite Simulation
Accelerated satellite orbital simulation using Physics-Informed Neural Networks (PINN) and Physics-Informed Recurrent Neural Networks (PI-RNN), eliminating dependency on expensive synthetic data generation while ensuring physical consistency.
Challenges
- Generating large volumes of synthetic orbital data using standard ODE solvers (RK4) was computationally expensive and time-intensive
- Models trained only on synthetic data failed to adapt to real satellite telemetry due to unmodeled effects (solar radiation pressure, atmospheric drag, magnetic perturbations)
- This created a modeling gap where trained models performed well on simulation but drifted when applied to real-world conditions
Solutions
- Implemented Physics-Informed Neural Network (PINN) to embed physical laws directly into the learning process
- Trained PINN simultaneously on real telemetry and physics-based loss, eliminating need for large synthetic datasets
- Used dummy collocation points to enforce physics consistency without costly data generation
- Developed Physics-Informed Recurrent Neural Network (PI-RNN) for orbital propagation, capturing temporal dynamics and handling maneuvers as control inputs
Outcomes
- Synthetic data generation became feasible on standard laptop GPU (NVIDIA RTX) - no need for high-end compute clusters
- Successfully merged real-world telemetry with physical constraints, leading to more stable and physically consistent synthetic data
- Maintained physical validity while reducing computational costs
- Enabled faster iteration cycles for model development and testing
Post-Maneuver Orbit Prediction & Tracking
Developed a probabilistic framework for predicting satellite orbits after unobserved maneuvers by progressively constraining an infinite maneuver space into a feasible prior distribution, then converging to precise orbit determination using Bayesian filtering and multi-hypothesis tracking.
Challenges
- After unobserved maneuvers, the satellite's new orbit is unknown — modeling all possible maneuvers produces an infinite-dimensional search space in RTN (Radial-Tangential-Normal) coordinates
- Brute-force exploration of the unconstrained maneuver space was computationally infeasible for real-time tracking and sensor tasking
- Ambiguous sensor measurements (radar, optical) with multiple unidentified objects required robust data association to link detections to the correct satellite state
- Needed to integrate physics constraints (two-body dynamics, J2 perturbations, drag, solar radiation pressure) with operational priors (fuel budgets, thruster hardware limits, mission intent)
Solutions
- Designed a 6-step progressive constraint pipeline: hardware magnitude bounds, fuel budget via Tsiolkovsky equation with LogNormal priors, anisotropic directional priors favoring tangential burns, timing/efficiency priors at optimal orbital phases, and mission-context weighting
- Implemented Monte Carlo profile generation using Dirichlet-distributed budget splitting across maneuver segments with configurable RTN direction sampling
- Built an orbital propagator integrating impulsive and finite-duration burns with RTN-to-ECI frame transformations for each maneuver segment
- Applied Unscented Kalman Filter (UKF) for Bayesian state estimation, collapsing the uncertainty cloud as measurements arrive
- Developed Joint Probabilistic Data Association (JPDA) and Multiple Hypothesis Tracking (MHT) running parallel UKFs to handle ambiguous multi-target measurement scenarios
Outcomes
- 99.99% reduction in search volume — from over 10 million km³ (unconstrained) to under 1,500 km³ through progressive constraint application
- Each constraint layer provided measurable reduction: fuel budget prior (~100% from baseline), individual ΔV prior (48.4% further), directional prior (25.8% further)
- Probabilistic framework enables rapid sensor tasking by focusing search on high-likelihood orbital regions rather than brute-force sky surveys
- Multi-hypothesis tracking (JPDA/MHT) provides robust orbit determination even with sparse, cluttered, or ambiguous measurements
- Implemented using Poliastro, Astropy, NumPy, and SciPy for production-grade orbital mechanics computations
Control Systems & State Estimation
Advanced control algorithms and state estimation techniques for autonomous systems, including Kalman filtering, model predictive control, and adaptive control strategies for dynamic environments.
Challenges
- Handling uncertainty and disturbances in real-world autonomous operations
- Achieving optimal performance while guaranteeing safety constraints
- Adapting control strategies to varying operational scenarios and environmental conditions
Solutions
- Implemented Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for nonlinear state estimation
- Developed model predictive control (MPC) with real-time constraint handling
- Created adaptive control algorithms that learn and adjust to changing system dynamics
- Built hierarchical decision-making architecture for mission planning and execution
Outcomes
- Robust state estimation even with noisy, uncertain measurements
- Smooth, precise autonomous operation in challenging conditions
- 100% safety compliance with zero constraint violations
- Successful deployment in GPS-denied and dynamic environments
Explore other solutions
AI Solutions
Advanced artificial intelligence solutions leveraging cutting-edge techniques for knowledge extraction, forecasting, and physics-aware modeling.
Robotics & Drones Solutions
Advanced robotic systems and autonomous drone solutions for automation, inspection, and data collection across various industries.
Ready to Transform Your Business?
Let's discuss how our solutions can solve your specific challenges.
Get in Touch