Peer-Reviewed Technical Communication Mixed Integer Nonlinear Programming Framework for Fixed Path Coordination of Multiple Underwater Vehicles Under Acoustic Communication Constraints

Mixed integer nonlinear programming (MINLP) techniques are increasingly used to address challenging problems in robotics, especially multivehicle motion planning (MVMP). The main contribution of this paper is a discrete time, distributed receding horizon mixed integer nonlinear programming (RH-MINLP) formulation of the underwater multivehicle path coordination problem with constraints on vehicle kinematics, dynamics, collision avoidance, and acoustic communication connectivity, and the application of state-of-the-art MINLP solution techniques. Each vehicle robot starts from a fixed start point and moves toward a goal point along a fixed path, so as to avoid collisions and remain in communication connectivity with other robots. Acoustic communication connectivity constraints account for the attenuation due to signal propagation and delays arising from multipath propagation in noisy communication environments, and specify intervehicle connectivity in terms of a signal-to-noise ratio (SNR) threshold. Scenarios including up to four robots are simulated to demonstrate: 1) the effect of communication connectivity requirements on robot velocity profiles; and 2) the dependence of the solution computation time on the communication connectivity requirement. Typically the optimization improved connectivity at no appreciable cost in journey time (as measured by the arrival time of the last arriving robot). Results also demonstrate the responsive nature of robot trajectories to safety requirements with collision avoidance being achieved at all times despite overlapping and intersecting paths.

[1]  M. Ani Hsieh,et al.  Experimental Multi-Vehicle Path Coordination under Communication Connectivity Constraints , 2012, ISER.

[2]  Moshe Kam,et al.  Robust communication connectivity for multi-robot path coordination using Mixed Integer Nonlinear Programming: Formulation and feasibility analysis , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Hanumant Singh,et al.  Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles , 2012, Int. J. Robotics Res..

[4]  Moshe Kam,et al.  Mathematical programming for Multi-Vehicle Motion Planning problems , 2012, 2012 IEEE International Conference on Robotics and Automation.

[5]  M. Stojanovic,et al.  Underwater Acoustic Communications: Design Considerations on the Physical Layer , 2008, 2008 Fifth Annual Conference on Wireless on Demand Network Systems and Services.

[6]  Antonio Pedro Aguiar,et al.  COORDINATED PATH-FOLLOWING CONTROL OF MULTIPLE AUVS IN THE PRESENCE OF COMMUNICATION FAILURES AND TIME DELAYS , 2006 .

[7]  Ryan M. Eustice,et al.  An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment , 2013, Robotics: Science and Systems.

[8]  Vijay Kumar,et al.  Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams , 2012, 2012 IEEE International Conference on Robotics and Automation.

[9]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[10]  Moshe Kam,et al.  Mathematical Programming Approaches for Multi-Vehicle Motion Planning: Linear, Nonlinear, and Mixed Integer Programming , 2013, Found. Trends Robotics.

[11]  Milica Stojanovic,et al.  Statistical characterization and capacity of shallow water acoustic channels , 2009, OCEANS 2009-EUROPE.

[12]  J. Vaganay,et al.  Ship hull inspection by hull-relative navigation and control , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[13]  Thierry Siméon,et al.  Path coordination for multiple mobile robots: a resolution-complete algorithm , 2002, IEEE Trans. Robotics Autom..

[14]  Hande Y. Benson,et al.  Using Interior-Point Methods within an Outer Approximation Framework for Mixed Integer Nonlinear Programming , 2012 .

[15]  N.M. Patrikalakis,et al.  Path Planning of Autonomous Underwater Vehicles for Adaptive Sampling Using Mixed Integer Linear Programming , 2008, IEEE Journal of Oceanic Engineering.

[16]  Gaurav S. Sukhatme,et al.  Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study , 2010, 2010 IEEE International Conference on Robotics and Automation.

[17]  Yan Pailhas,et al.  Path Planning for Autonomous Underwater Vehicles , 2007, IEEE Transactions on Robotics.

[18]  P. Encarnacao,et al.  3D path following for autonomous underwater vehicle , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[19]  Igor Skrjanc,et al.  Time optimal path planning considering acceleration limits , 2003, Robotics Auton. Syst..

[20]  Franz S. Hover,et al.  Collaborative bathymetry-based localization of a team of autonomous underwater vehicles , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Junku Yuh,et al.  Design and Control of Autonomous Underwater Robots: A Survey , 2000, Auton. Robots.

[22]  Yvan Petillot,et al.  Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar , 2001 .

[23]  Milica Stojanovic,et al.  On the relationship between capacity and distance in an underwater acoustic communication channel , 2007, MOCO.

[24]  Eric Feron,et al.  Decentralized Cooperative Trajectory Planning of Multiple Aircraft with Hard Safety Guarantees , 2004 .

[25]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..