# Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

arXiv: https://arxiv.org/abs/2010.10190 · HTML: https://mlsanity.com/paper/2010.10190/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement](https://mlsanity.com/paper/2205.11853/) (2022)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles](https://mlsanity.com/paper/2112.11217/) (2021)
- [Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic](https://mlsanity.com/paper/2310.02918/) (2023)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (2025)
- [Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments](https://mlsanity.com/paper/2402.16449/) (2024)
- [Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping](https://mlsanity.com/paper/2411.04915/) (2024)
- [CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2603.06921/) (2026)
- [Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning](https://mlsanity.com/paper/2606.08136/) (2026)
- [Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments](https://mlsanity.com/paper/2209.14233/) (2022)
- [Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning](https://mlsanity.com/paper/2308.12069/) (2023)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation](https://mlsanity.com/paper/2503.15127/) (2025)
- [Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications](https://mlsanity.com/paper/2507.23350/) (2025)
- [Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction](https://mlsanity.com/paper/2508.07079/) (2025)
- [Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving](https://mlsanity.com/paper/2512.03774/) (2025)
- [JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems](https://mlsanity.com/paper/2606.20686/) (2026)
- [PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation](https://mlsanity.com/paper/2607.10288/) (2026)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)
- [CoCoNav: Conformal Control for Safe Robot Navigation in Crowds](https://mlsanity.com/paper/2608.07751/) (2026)

## Abstract (arXiv)

This paper presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.