Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity. However, caster misalignment during low-speed or high-curvature maneuvers induces strong contact nonlinearities, widening the planning-execution gap between commanded (v,ω) and realized motion. Existing caster-aware strategies are highly sensitive to state-estimation reliability, causing unstable execution in uncertain transition regimes. To address this, we present Multi-Stage Caster-aware Control (MSC), a planning-execution integrated hierarchical framework featuring two core mechanisms. First, an uncertainty-aware NMPC (CU-MPC) dynamically schedules caster-aware costs and input regularization using predictive uncertainty to suppress overly aggressive maneuvers. Second, a reinforcement learning-based execution layer estimates and predicts caster states under partial observability, transforming high-level velocity commands into physically executable wheel speeds adapted to current contact conditions. Validations in Isaac Sim and real robots demonstrate that MSC offers a clear comparative advantage over existing decoupled compensation approaches by significantly improving caster-state estimation accuracy, minimizing misalignment, and enhancing tracking robustness in caster-sensitive maneuvers.
Figures & tables
Fig. 1: Caster misalignment and its navigation impact. A mismatch between the commanded motion direction and the caster yaw angle direction generates a lateral scrub contact force and a resistive caster-induced reaction torque, contributing to a planning–execution gap between planned (v,ω) and physically realizable motions.
Fig. 2: Overall framework of MSC. The caster-uncertainty-aware CU-MPC generates local velocity references. An RL-based Execution Layer, trained with privileged information in simulation, estimates/predicts caster states with uncertainty and transforms the high-level (v,ω) commands into dynamically feasible wheel commands. This hierarchical integration mitigates the planning–execution gap arising from caster-induced contact nonlinearities.
Fig. 3: Trajectory tracking comparison against baselines. Proposed Method improves tracking smoothness and accuracy over B1/B2, particularly under caster-induced contact nonlinearities.
Parameter
Symbol
Value
MPC (CA-MPC / CU-MPC)
NMPC horizon
N
63
Sampling time
Δt
0.05
State weight (tracking)
Q
1.0
Input weight (base)
R
0.1
Caster-aware weight
wcw0
1.0
TABLE I: Key parameters used in simulation, learning, and MPC.
Fig. 4: Caster estimation/prediction and uncertainty-aware scheduling (in-place low-speed rotation). (a) Left caster angle comparison: GT, kinematic estimator, learned estimator, and learned predictor with the prediction uncertainty band. (b) CU-MPC weight scheduling driven by the predicted risk/uncertainty ρ : caster-aware weight wcw and control regularization weight wctrl .
Fig. 5: Real-world experimental results (C-path). (a) Executed trajectories for the compared methods in the mapped environment. (b) Caster angle estimation compared against encoder ground truth (GT), highlighting the phase lag of the kinematics-based estimator and the improved agreement of the proposed learning-based estimator. (c) Linear velocity tracking error ev over time. (d) Angular velocity tracking error eω over time.
Metric
M-path
S-path
C-path
B1
B2
Ours
B1
B2
Ours
B1
B2
Ours
RMSE [d] (m)
0.154
0.080
0.094
0.213
0.211
0.092
0.213
0.058
0.043
RMSE [eψ] (rad)
0.532
0.472
0.533
0.173
0.474
0.176
0.127
0.184
0.129
RMSE [v] (m/s)
0.007
0.017
0.019
0.006
0.020
0.013
0.006
0.013
0.011
RMSE [ω] (rad/s)
0.069
0.045
0.029
0.074
0.057
0.032
0.068
0.027
0.024
RMSE [eφ^] (rad)
0.301
0.276
0.029
0.289
0.425
0.065
0.390
0.712
0.064
TABLE II: Simulation results in Isaac Sim (M/S/C paths).
Fig. 6: Real-world experimental setup. (a) The AMR hardware platform used in the experiments. (b) ROS 2-based navigation stack visualization during execution (planning/localization and trajectory tracking).
Robot trajectory tracking develops methods that make robots follow desired paths or motions accurately despite disturbances, uncertainty, and physical constraints.
This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. We develop a single-loop and a dual-loop MPC; the latter embeds a stabilizing feedback controller within the inner loop, yielding a linear time-invariant system. Both controllers are implemented with CasADi - including automatic code generation - evaluated across various solvers, and validated within the Basilisk astrodynamics simulation framework. The experimental results demonstrate improved tracking accuracy alongside reductions in computational effort and memory consumption. Finally, embedded delivery to an ARM Cortex-M7 - representative of commercial off-the-shelf devices used in New Space platforms - confirms the real-time feasibility of these approaches and highlights their suitability for onboard attitude control in resource-constrained spacecraft rendezvous missions.
Peter Stadler, Alexander Meinert, Niklas Baldauf +1
Space Applications Group, e:fs TechHub GmbH, 85080 Gaimersheim, Germany · Faculty of Electrical Engineering and Information Technology, Ingolstadt University of Applied Sciences, Germany
This paper studies the constrained line-of-sight pointing control problem of a two-axis current-command gimbal subjected to mismatched mechanical disturbances. Friction, torque ripple, current-response lag, model mismatch, and limited drive commands make accurate pointing more challenging yet interesting. A quantile disturbance predictor is proposed to compensate predictable acceleration residuals and describe their remaining uncertainty, with the aid of learning median and outer quantiles from state, command, and reference histories. A tube-inspired model predictive controller is proposed to reserve command headroom according to prediction uncertainty, with the aid of calibrated radii and stage-dependent input margins. The median improves the nominal prediction, while the radius adapts the command reserve without adding optimization variables. A finite-horizon tube inclusion result is established for a specified linear error model and fixed ancillary policy; its conditions are distinguished from the approximations used in hardware. Native command bounds and a final command limiter keep the sent commands within their prescribed range. On a two-axis embedded platform, four controller configurations are compared in seven repeated trials each. The proposed configuration achieves a two-dimensional pointing root-mean-square error of 3.94±0.10 mrad with no command-limit samples in the evaluation windows. Its performance supports the combined use of residual prediction and adaptive command margins in the tested operating condition. Solver times and logged loop overruns are reported separately for the nominal 100 Hz implementation.
Yaokun Lu, Yifan Li, Enmei Wang +3
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China · Hangzhou Innovation Institute of Beihang University, Hangzhou, 311115, China · School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, 100191, China
Maintaining small path-tracking errors under moving reference frames is essential for robotic manipulation tasks with tight execution tolerances, such as machining and surgical automation. Conventional time-indexed controllers, including tracking Model Predictive Control (MPC), rely on a fixed temporal correspondence with the reference trajectory and may therefore accumulate path-relative error under abrupt reference-frame motion or timing mismatch. This paper presents MR-MPCC, a Model Predictive Contouring Control (MPCC) framework for velocity-controlled robotic manipulators with 6-DoF end-effector pose regulation under Moving-Reference disturbances. A hierarchical control architecture is adopted, in which an upper-level MPCC optimizes path progress while penalizing contouring and orientation errors, and a constrained resolved motion rate control (RMRC) layer maps the commanded Cartesian twist to feasible joint velocities under joint-level and safety constraints. MR-MPCC is implemented in a ROS2--MuJoCo pipeline and validated on a UR5e platform under moving-reference disturbances, where the path frame is perturbed and tracked by electromagnetic sensors. Compared with PI control and time-indexed tracking MPC under matched disturbance, MR-MPCC achieves lower geometric path-following error and smaller deviations in the moving path frame, demonstrating improved path-following performance under reference-frame motion.
Chonghao Cheng, Tiancheng Li, Jonathan Woolfrey +2
Robotics Institute, University of Technology Sydney, Sydney, NSW, Australia. · School of Informatics, University of Edinburgh, Edinburgh, UK.