Autonomous Mobile Navigation MPC — Competition-Proven Control for Ground Vehicles and Mobile Robots

Public reference use case (by others): This page summarizes publicly available reference implementations and papers. Not client results of Dr. Rafal Noga.

Why This Matters (Executive Summary)

• Business pain: Autonomous ground vehicles and mobile robots struggle with real-time obstacle avoidance, path accuracy under sensor noise, and safe operation at the limits of vehicle dynamics — leading to slow speeds, conservative operation, or collisions.
• Solution class: Model Predictive Control provides a unified estimation-planning-control architecture that explicitly handles actuator limits, dynamic constraints, and obstacle avoidance within a single optimization framework.
• Measurable outcomes: Published implementations report lap time reductions of approximately 10% through learning-based MPC, obstacle avoidance at speeds up to 11.5 km/h via GPU-accelerated sampling, and reliable vision-based navigation over 1.8 km outdoor paths.
• Industrial transfer: The same MPC stack proven in autonomous racing transfers directly to industrial AGVs/AMRs, inspection robots, and outdoor mobile platforms — the architecture is the same, only the dynamics model and constraints change.

The Design Pattern Explained

Autonomous mobile navigation MPC follows a layered architecture: state estimation (localization, mapping) feeds a trajectory planner (path or trajectory optimization), which feeds an MPC controller that computes actuator commands while respecting vehicle dynamics and safety constraints. A safety supervisor monitors constraint compliance at each layer.

MPC is preferred over PID or pure-pursuit alternatives because it can simultaneously handle nonlinear vehicle dynamics (tire saturation, lateral acceleration limits), hard actuator constraints (steering rate, motor torque), and obstacle avoidance — all within a single receding-horizon optimization. At the performance limit, exploiting the full nonlinear dynamics via NMPC yields measurably better lap times and tighter path tracking than any decoupled controller architecture.

Key engineering principles include: (1) model the dominant dynamics including actuator lag, (2) encode safety as hard constraints rather than penalty terms, (3) use learning or adaptation to compensate model mismatch online, and (4) validate incrementally from simulation through low-speed tests to full-speed deployment.

Applications & Reference Implementations

Application 1: AMZ Driverless — Competition-Proven Formula Student Racing

The AMZ Driverless team at ETH Zurich developed a full autonomous racing system integrating perception, planning, and MPC-based control for the Formula Student Driverless competition. The system demonstrated robust performance under traction and track constraints, with successful competition participation and published event time results. The architecture covers the complete pipeline from cone detection through trajectory planning to constraint-aware control at the vehicle’s handling limits. This work validates the full-stack MPC approach under competitive time pressure and real-world uncertainty. ¹

Application 2: Learning-Based MPC at the Racing Limit — 10% Lap Time Reduction

Researchers at ETH Zurich combined a contouring MPC formulation with Gaussian Process learning of model error on a full-size autonomous race car. The system operated at speeds of 15 m/s with lateral accelerations up to 2 g, pushing the vehicle to its handling limits. After online learning, the approach achieved approximately 10% lap time reductions compared to the nominal model. A dictionary-style data management approach enabled continual model updates during operation, demonstrating that learning-based MPC can safely exploit performance margins that a conservative fixed model would leave on the table. ²

Application 3: GPU-Accelerated Randomized MPC — Dynamic Obstacle Avoidance at 11.5 km/h

A randomized MPC implementation on a 1/10 scale RC car demonstrated the impact of computational acceleration on real-time obstacle avoidance. The CPU-only implementation achieved a maximum of 30 Hz and could only avoid obstacles at 3.6 km/h, colliding at 5.1 km/h. Moving to GPU acceleration with 1000 trajectory samples enabled 200 Hz control rates and smooth obstacle avoidance at speeds up to 11.5 km/h. The prediction horizon of N=30 steps provided approximately 3 m of look-ahead. This result quantifies the direct relationship between compute budget and achievable safe speed in sampling-based MPC. ³

Application 4: Outdoor Cleaning Robot — MPCC Retrofit for Path Tracking

A manual outdoor sweeper was retrofitted into an autonomous cleaning robot using Model Predictive Contouring Control (MPCC) for path tracking. The MPCC formulation balances the trade-off between trajectory accuracy and progress speed via tunable cost weights, allowing operators to prioritize precision or throughput depending on the cleaning task. Simulation and experimental results validated improved path-tracking performance on the existing platform. This case demonstrates that MPC can be retrofitted onto existing mobile equipment without redesigning the vehicle, making it directly relevant to industrial fleet upgrades. ⁴

Application 5: Vision-Based NMPC Over 1.8 km — Learning Compensates Model Mismatch

A learning-based Nonlinear MPC was evaluated on two mobile robot platforms (50 kg and 160 kg) for vision-based path tracking over extended outdoor distances. Field trials covered 1.8 km and 500 m paths at speeds up to 1.6 m/s, relying on camera-based localization without LIDAR. The learning component compensated for model mismatch and changing terrain conditions, maintaining tracking accuracy over the full distance. This validates NMPC for sensor-limited outdoor platforms where GPS or LIDAR may not be available or cost-effective. ⁵

Application 6: Ballbot Path Following — MPC on a Dynamically Unstable Platform

A path-following MPC was implemented on a ballbot (dynamically unstable balancing robot) with obstacle avoidance constraints. The MPC ran at 10 Hz on an external computer, executing circle tracking at a radius of 1 m with a desired speed of 0.25 m/s while navigating around obstacle configurations. The ballbot case is significant because it demonstrates MPC handling both stabilization and navigation simultaneously on an inherently unstable platform — a pattern that transfers to any system where balance and path-following must coexist. ⁶

What This Means for Your Operations

The autonomous navigation MPC pattern is directly transferable to industrial AGVs, AMRs, inspection robots, and outdoor mobile platforms used in manufacturing, logistics, and facility management. If your current mobile robots operate at conservative speeds, struggle with obstacle avoidance, or require excessive manual intervention, the estimation-planning-MPC architecture offers a systematic upgrade path.

Common readiness indicators include: existing mobile platforms with programmable controllers, reliable position/velocity measurements (encoders, IMU, cameras), defined operating environments with known constraints, and a willingness to invest in model identification and staged validation.

How We Deliver This (Engagement Model)

• Phase 0: NDA + data request — share platform specifications, sensor configuration, operating environment, and current control architecture.
• Phase 1: Fixed-scope discovery (2-4 weeks) — vehicle dynamics identification, constraint mapping, feasibility assessment, and concept design for the MPC stack.
• Phase 2: Implementation + validation + commissioning — MPC development, simulation validation, low-speed testing, and progressive speed/complexity ramp-up.
• Phase 3: Monitoring + training + scaling — operator training, performance dashboards, and extension to additional platforms or operating scenarios.

Typical KPIs to Track

• Safety: Collision rate, constraint violation frequency, emergency stop triggers
• Performance: Path tracking error (lateral deviation), achievable speed, lap/cycle time
• Efficiency: Energy consumption per mission, idle time, operator interventions per shift
• Robustness: Performance degradation under sensor noise, model mismatch, or environmental change

Risks & Prerequisites

• Model quality: MPC performance depends on a dynamics model that captures the dominant vehicle behavior; tire models and actuator dynamics must be validated experimentally.
• Compute budget: Real-time MPC requires sufficient onboard or edge compute — GPU acceleration may be needed for sampling-based approaches or high control rates.
• Sensor reliability: Vision-based systems degrade in poor lighting or adverse weather; sensor fusion strategies should be planned.
• Safety layer: MPC should operate within a safety supervision framework that can override or halt the vehicle if constraint violations are detected.
• Incremental validation: Never skip low-speed testing — validate the full stack at reduced speed and complexity before pushing to operational limits.

FAQ

Q: Can MPC be retrofitted onto our existing mobile robots, or do we need new hardware? A: Multiple published cases demonstrate MPC retrofitted onto existing platforms (cleaning robots, RC cars, wheeled robots) without hardware redesign. The key requirement is a programmable controller interface and reliable sensor feedback.

Q: How does MPC handle unexpected obstacles that are not in the map? A: MPC with obstacle avoidance constraints can react to dynamically detected obstacles within its prediction horizon. GPU-accelerated sampling approaches enable real-time replanning at rates up to 200 Hz, providing sub-second reaction times.

Q: What is the typical compute requirement for mobile robot MPC? A: Depending on the formulation, MPC runs at 10-200 Hz on hardware ranging from embedded ARM processors to NVIDIA Jetson GPUs. The compute requirement scales with prediction horizon length, number of constraints, and whether the formulation is convex or nonlinear.

Q: How long does it take to commission MPC on a new vehicle platform? A: A typical project from model identification through validated deployment takes 2-4 months, depending on platform complexity and the availability of existing dynamics models and sensor infrastructure.

Book a 30-Minute Discovery Call

Ready to explore whether this pattern fits your system?

Dr. Rafal Noga — Independent APC/MPC Consultant

📧 Email me · 🌐 noga.es

Fixed-scope discovery · NDA-first · DACH on-site available

Public References

Footnotes

1. Kabzan et al., “AMZ Driverless: The Full Autonomous Racing System” (arXiv, 2019). https://arxiv.org/pdf/1905.05150.pdf ↩

2. Kabzan et al., “Learning-Based Model Predictive Control for Autonomous Racing” (ETH Research Collection). https://www.research-collection.ethz.ch/bitstreams/7d0faa11-1667-481c-a497-ca7ef4611521/download ↩

3. Muraleedharan et al., “Randomized MPC for Dynamic Obstacle Avoidance and Autonomous Racing” (IEEE T-IV, 2022). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9911860 ↩

4. “Autonomous Outdoor Cleaning Robot with MPCC Path Tracking” (International Journal of Automation Technology). https://www.jstage.jst.go.jp/article/ijat/19/6/19_1086/_pdf ↩

5. Ostafew et al., “Learning-Based Nonlinear MPC to Improve Vision-Based Mobile Robot Path Tracking” (ICRA, 2014). https://asrl.utias.utoronto.ca/wp-content/papercite-data/pdf/ostafew_icra14.pdf ↩

6. “Path-Following MPC for Ballbots” (arXiv, 2020). https://arxiv.org/pdf/2008.10383.pdf ↩