Realtime Simulation-in-the-Loop Control for Agile Ground Vehicles

In this paper we present a system for real-time control of agile ground vehicles operating in rough 3D terrain replete with bumps, berms, loop-the-loops, skidding, banked-turns and large jumps. The proposed approach fuses local-planning and feedback trajectory-tracking in a unified, simulation-based framework that operates in real-time. Experimentally we find that fast physical simulation-in-the-loop enables impressive control over difficult 3D terrain. The success of the proposed method can be attributed to the fact that it takes advantage of the full expressiveness of the inherently non-linear, terrain-dependent, highly dynamic systems involved. Performance is experimentally validated in a motion capture lab on a high-speed non-holonomic vehicle navigating a 3D map provided by an offline perception system.

[1]  Alonzo Kelly,et al.  Kinodynamic motion planning with state lattice motion primitives , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  S. LaValle Rapidly-exploring random trees : a new tool for path planning , 1998 .

[3]  John T. Wen,et al.  Trajectory tracking control of a car-trailer system , 1997, IEEE Trans. Control. Syst. Technol..

[4]  Hans B. Pacejka,et al.  Tire and Vehicle Dynamics , 1982 .

[5]  Gabriel Hugh Elkaim,et al.  Contin uous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles , 2010 .

[6]  V. V. Bolotin,et al.  Mechanical Engineering Series , 2001 .

[7]  Nancy M. Amato,et al.  A randomized roadmap method for path and manipulation planning , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[8]  Giancarlo Genta,et al.  Motor Vehicle Dynamics: Modeling and Simulation , 1997, Series on Advances in Mathematics for Applied Sciences.

[9]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[10]  Gabe Sibley,et al.  A holistic framework for planning , real-time control and model learning for high-speed ground vehicle navigation over rough 3 D terrain , 2012 .

[11]  Aaron Nathan,et al.  Cornell University's 2005 DARPA Grand Challenge Entry , 2006 .

[12]  Lydia E. Kavraki,et al.  A Random Sampling Scheme for Path Planning , 1997, Int. J. Robotics Res..

[13]  Tor Arne Johansen,et al.  Computation of Lyapunov functions for smooth nonlinear systems using convex optimization , 2000, Autom..

[14]  John J. Leonard,et al.  Cooperative AUV Navigation Using a Single Surface Craft , 2009, FSR.

[15]  Alonzo Kelly,et al.  Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots , 2007, Int. J. Robotics Res..

[16]  Thomas W. Sederberg,et al.  COMPUTER AIDED GEOMETRIC DESIGN , 2012 .

[17]  Russ Tedrake,et al.  LQR-trees: Feedback motion planning on sparse randomized trees , 2009, Robotics: Science and Systems.

[18]  Claire J. Tomlin,et al.  Extensions of learning-based model predictive control for real-time application to a quadrotor helicopter , 2012, 2012 American Control Conference (ACC).

[19]  Emilio Frazzoli,et al.  Incremental Sampling-based Algorithms for Optimal Motion Planning , 2010, Robotics: Science and Systems.

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