Event-based estimation and control for remote robot operation with reduced communication

An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster-response scenarios such as the DARPA Robotics Challenge.

[1]  E. Todorov,et al.  A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[2]  Pierre-Brice Wieber,et al.  Fast Direct Multiple Shooting Algorithms for Optimal Robot Control , 2005 .

[3]  D. Mayne A Second-order Gradient Method for Determining Optimal Trajectories of Non-linear Discrete-time Systems , 1966 .

[4]  C. Cassandras The event-driven paradigm for control, communication and optimization , 2014, J. Control. Decis..

[5]  Michael D. Lemmon,et al.  Event-Triggered Feedback in Control, Estimation, and Optimization , 2010 .

[6]  R. Bellman Dynamic programming. , 1957, Science.

[7]  Mark W. Spong,et al.  Bilateral teleoperation: An historical survey , 2006, Autom..

[8]  H. Kappen Path integrals and symmetry breaking for optimal control theory , 2005, physics/0505066.

[9]  Dmitry Berenson,et al.  Toward a user-guided manipulation framework for high-DOF robots with limited communication , 2013, TePRA.

[10]  Stefan Schaal,et al.  A Generalized Path Integral Control Approach to Reinforcement Learning , 2010, J. Mach. Learn. Res..

[11]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[12]  Craig Sayers,et al.  Remote control robotics , 1998 .

[13]  S. Trimpe,et al.  The Balancing Cube: A Dynamic Sculpture As Test Bed for Distributed Estimation and Control , 2012, IEEE Control Systems.

[14]  Dmitry Berenson,et al.  Toward a user-guided manipulation framework for high-DOF robots with limited communication , 2013, 2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA).

[15]  Joris Sijs,et al.  Relevant Sampling Applied to Event-Based State-Estimation , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[16]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Vol. II , 1976 .

[17]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[18]  Sebastian Trimpe,et al.  Event-based state estimation with variance-based triggering , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[19]  Raffaello D'Andrea,et al.  An Experimental Demonstration of a Distributed and Event-Based State Estimation Algorithm , 2011 .

[20]  Dimitri P. Bertsekas,et al.  Dynamic programming & optimal control , volume i , 2014 .

[21]  Paulo Tabuada,et al.  An introduction to event-triggered and self-triggered control , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[22]  Sebastian Trimpe,et al.  Event-Based State Estimation With Variance-Based Triggering , 2012, IEEE Transactions on Automatic Control.

[23]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[24]  Sandra Hirche,et al.  Human-Oriented Control for Haptic Teleoperation , 2012, Proceedings of the IEEE.