Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

This letter 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.

[1]  Javier Alonso-Mora,et al.  Multi-robot formation control and object transport in dynamic environments via constrained optimization , 2017, Int. J. Robotics Res..

[2]  Jonathan P. How,et al.  Safe Reinforcement Learning With Model Uncertainty Estimates , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[3]  Kai Oliver Arras,et al.  On multi-modal people tracking from mobile platforms in very crowded and dynamic environments , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[4]  D. Rus,et al.  Annual Review of Control , Robotics , and Autonomous Systems Planning and Decision-Making for Autonomous Vehicles , 2018 .

[5]  Roland Siegwart,et al.  Cooperative Collision Avoidance for Nonholonomic Robots , 2018, IEEE Transactions on Robotics.

[6]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[7]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[8]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[9]  Alberto Bemporad,et al.  Predictive Control for Linear and Hybrid Systems , 2017 .

[10]  Javier Alonso-Mora,et al.  Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model , 2018, IEEE Transactions on Intelligent Transportation Systems.

[11]  Wei Zhan,et al.  Constrained iterative LQR for on-road autonomous driving motion planning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[12]  P. Molnár.  Social Force Model for Pedestrian Dynamics Typeset Using Revt E X 1 , 1995 .

[13]  Rachid Alami,et al.  Human-aware robot navigation: A survey , 2013, Robotics Auton. Syst..

[14]  Jaime F. Fisac,et al.  Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Anca D. Dragan,et al.  Probabilistically Safe Robot Planning with Confidence-Based Human Predictions , 2018, Robotics: Science and Systems.

[16]  Alexei Yu. Uteshev,et al.  Metric problems for quadrics in multidimensional space , 2015, J. Symb. Comput..

[17]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[18]  Pete Trautman,et al.  Sparse interacting Gaussian processes: Efficiency and optimality theorems of autonomous crowd navigation , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[19]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[20]  Chung Choo Chung,et al.  On-Road Path Generation and Control for Waypoints Tracking , 2017, IEEE Intelligent Transportation Systems Magazine.

[21]  Alonzo Kelly,et al.  Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths , 2009, FSR.

[22]  Christian Kirches,et al.  qpOASES: a parametric active-set algorithm for quadratic programming , 2014, Mathematical Programming Computation.

[23]  Francesco Borrelli,et al.  MPC-Based Approach to Active Steering for Autonomous Vehicle Systems , 2005 .

[24]  Gonzalo Ferrer,et al.  Robot companion: A social-force based approach with human awareness-navigation in crowded environments , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[26]  Hao Zhang,et al.  Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[27]  Marco Pavone,et al.  A convex optimization approach to smooth trajectories for motion planning with car-like robots , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[28]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[29]  Dariu Gavrila,et al.  SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[30]  Chris Manzie,et al.  Model predictive contouring control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[31]  J. Christian Gerdes,et al.  Safe driving envelopes for path tracking in autonomous vehicles , 2017 .

[32]  Jonathan P. How,et al.  Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[33]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[34]  Francesco Borrelli,et al.  Kinematic and dynamic vehicle models for autonomous driving control design , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[35]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .