Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators.  Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for UKF-based multi-sensor networks that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques, since it implements an optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence   when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator’s performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab’s Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations.

To reliably localize and control wheeled autonomous rovers, their controllers must keep the wheels away from traction loss. In this paper, we develop a fast and practical traction control system for rovers that track dynamic trajectories on rough firm terrains, leveraging their normally existing redundant control directions. Trajectory-tracking performance is guaranteed by input-output linearizing a nonholonomic model of the system and employing an appropriate stabilizing control law. We propose a novel methodology to optimally lift the control signals at the rover’s output level to determine the control actions that enhance the system’s traction without affecting the tracking performance. The methodology uses the knowledge of wheels’ friction coefficients and estimation of normal and tractive forces based on a nonholonomic rover model to optimally distribute the tractive forces among the wheels. The novelty is in redefining the optimization problem in both lateral and longitudinal directions that require minimum information about wheel-ground interactions and leads to linear optimality conditions. We define the notion of total required force/moment at system’s center of mass to (i) introduce reference directions for tractive forces in the proposed cost functions, and (ii) identify the rover wheels fighting against the motion. To prevent wheel-fighting, we find sub-optimal solutions that suppress tractive forces at the fighting wheels. The proposed traction control system is implemented on a six-wheel autonomous Lunar rover and its efficacy is investigated by a developed software-in-the-loop simulation environment using Vortex Studio. This software simulates a 3-dimensional digital twin of the system, with different terrain and tire model options. When compared to the conventional pseudo-inverse solution, the developed traction controller demonstrates improved overall traction and it saves the rover from traction loss.