An adaptive dynamic controller for autonomous mobile robot trajectory tracking

This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control RC-LB-NMPC algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.

Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking

This paper presents a Learning-based Nonlinear Model Predictive Control LB-NMPC algorithm to achieve high-performance path tracking in challenging off-road terrain through learning The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model Disturbances are modeled as a Gaussian process GP as a function of system state, input, and other relevant variables The GP is updated based on experience collected during previous trials Localization for the controller is provided by an onboard, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments The paper presents experimental results including over 3i¾?km of travel by three significantly different robot platforms with masses ranging from 50 to 600 kg and at speeds ranging from 035 to 12 m/s associated video at http://tinycc/RoverLearnsDisturbances Planned speeds are generated by a novel experience-based speed scheduler that balances overall travel time, path-tracking errors, and localization reliability The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle- and trajectory-specific path-tracking errors based on experience

/pdf/learning-based-nonlinear-model-predictive-control-to-improve-30yr4bdbfh.pdf

Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

Nonlinear Model Predictive Control for trajectory tracking and collision avoidance of underactuated vessels with disturbances

This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm for an au- tonomous mobile robot to reduce path-tracking errors over repeated traverses along a reference path. The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) based on experience collected during previous traversals as a function of system state, input and other relevant variables. Modelling the disturbance as a GP enables interpolation and extrapolation of learned disturbances, a key feature of this algorithm. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environ- ments. The paper presents experimental results including over 1.8 km of travel by a four-wheeled, 50 kg robot travelling through challenging terrain (including steep, uneven hills) and by a six-wheeled, 160 kg robot learning disturbances caused by unmodelled dynamics at speeds ranging from 0.35 m/s to 1.0 m/s. The speed is scheduled to balance trial time, path- tracking errors, and localization reliability based on previous experience. The results show that the system can start from a generic a priori vehicle model and subsequently learn to reduce vehicle- and trajectory-specific path-tracking errors based on experience.

https://www.dynsyslab.org/wp-content/papercite-data/pdf/ostafew-icra14.pdf

Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments

This work focus on the application of model-based predictive control (MPC) to the trajectory tracking problem of nonholonomic wheeled mobile robots (WMR). The main motivation of the use of MPC in this case relies on its ability in considering, in a straightforward way, control a nd state constraints that naturally arise in practical proble ms. Furthermore, MPC techniques consider an explicit performance criterion to be minimized during the computation of the control law. The trajectory tracking problem is solved using two approaches: (1) nonlinear MPC and (2) linear MPC. Simulation results are provided in order to show the effectiveness of both schemes. Considerations regarding the computational effort of the MPC are developed with the purpose of analyzing the real-time implementation viability of the proposed techniques.

/pdf/mobile-robot-trajectory-tracking-using-model-predictive-44a5sfslcu.pdf

João Manoel

Papers

Mobile robot trajectory tracking using model predictive control