Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control

This work presents a method for multi-robot trajectory planning and coordination based on nonlinear model predictive control (NMPC). In contrast to centralized approaches, we consider the distributed case where each robot has an on-board computation unit to solve a local NMPC problem and can communicate with other robots in its neighborhood. We show that, thanks to tailored interactions (i.e., interactions designed according to a nonconvex alternating direction method of multipliers, or ADMM, scheme), the proposed solution is equivalent to solving the centralized control problem. With some communication exchange, required by the ADMM scheme at given synchronization steps, the safety of the robots is preserved, that is, collisions with neighboring robots are avoided and the robots stay within the bounds of the environment. In this work, we tested the proposed method to coordinate three autonomous vessels at a canal intersection. Nevertheless, the proposed approach is general and can be applied to different applications and robot models.

[1]  Domitilla Del Vecchio,et al.  Cooperative Collision Avoidance at Intersections: Algorithms and Experiments , 2013, IEEE Transactions on Intelligent Transportation Systems.

[2]  Javier Alonso-Mora,et al.  Parallel autonomy in automated vehicles: Safe motion generation with minimal intervention , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Huarong Zheng,et al.  Coordination of waterborne AGVs , 2016 .

[4]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[5]  Wotao Yin,et al.  Global Convergence of ADMM in Nonconvex Nonsmooth Optimization , 2015, Journal of Scientific Computing.

[6]  Henk Wymeersch,et al.  An Asynchronous Algorithm for Optimal Vehicle Coordination at Traffic Intersections , 2017 .

[7]  Chris Manzie,et al.  Model Predictive Contouring Control for Biaxial Systems , 2013, IEEE Transactions on Control Systems Technology.

[8]  Domitilla Del Vecchio,et al.  Least Restrictive Supervisors for Intersection Collision Avoidance: A Scheduling Approach , 2015, IEEE Transactions on Automatic Control.

[9]  Rudy R. Negenborn,et al.  Fast ADMM for Distributed Model Predictive Control of Cooperative Waterborne AGVs , 2017, IEEE Transactions on Control Systems Technology.

[10]  Rudy R. Negenborn,et al.  Cooperative Distributed Collision Avoidance Based on ADMM for Waterborne AGVs , 2015, ICCL.

[11]  Stefano Di Cairano,et al.  The development of Model Predictive Control in automotive industry: A survey , 2012, 2012 IEEE International Conference on Control Applications.

[12]  Roland Siegwart,et al.  Relaxing the planar assumption: 3D state estimation for an autonomous surface vessel , 2015, Int. J. Robotics Res..

[13]  Javier Alonso-Mora,et al.  A message-passing algorithm for multi-agent trajectory planning , 2013, NIPS.

[14]  Domitilla Del Vecchio,et al.  Safety Verification and Control for Collision Avoidance at Road Intersections , 2016, IEEE Transactions on Automatic Control.

[15]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[16]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[17]  J. Xin,et al.  Control and Coordination for Automated Container Terminals , 2015 .

[18]  Henk Wymeersch,et al.  Traffic Coordination at Road Intersections: Autonomous Decision-Making Algorithms Using Model-Based Heuristics , 2017, IEEE Intelligent Transportation Systems Magazine.

[19]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[20]  Tamas Keviczky,et al.  Operator-Splitting and Gradient Methods for Real-Time Predictive Flight Control Design , 2017 .