This article proposes a framework to address the challenges of the uncalibrated cylinder-driven heavy-legged robot (HLR) in accurately observing the ground reaction force (GRF). The proposed framework eliminates the need for force/torque sensors mounted on end-effectors or joints. One key contribution of this article is the development of a combined model, referred to as an approximate PMSM model (APM), which incorporates permanent magnet synchronous motors (PMSMs), electric cylinders, and the HLR. This model establishes the relationship between the input phase currents and the movement of the HLR, and it captures the characteristics of GRF, the HLR nominal torque, and the overall disturbances. To enable GRF observation based on the measured currents, an improved sliding-mode observer with harmonic, nominal, and unmodeled compensation was used. Harmonic compensation enhanced real-time responses and accuracy. Additionally, a radial basis function neural network was used to compensate for the unmodeled portion, which includes friction in all drive components of the HLR. Subsequently, a modified form of the nonlinear disturbance observer compensation was introduced to account for the HLR nominal torque in the APM. Through experimental evaluation, the effectiveness of the proposed framework was validated for the GRF observation.

Wheel-legged robots (WLRs) with knee-wheel placement can switch motion mode into wheeled motion and legged motion depending on the road condition. However, in wheeled motion mode, robots are energy-efficiency and high maneuverability but struggle to handle rough terrain. To address this challenge, we propose a quarter-car model predictive controller (MPC) that utilizes both thigh and calf leg actuation to track sprung mass displacement and regulate tire force during unknown road disturbances. To overcome the high computational demands of whole-body MPC-based posture control for the entire robot, we propose a distributed MPC-based framework with inverse kinematics. Simulations, including quarter-car wheel-legged system simulation, posture tracking, braking, and steering with load transfer, were conducted to evaluate the effectiveness of our proposed control framework and the promising performance of multi-actuation involving the calf leg. The results of this paper demonstrate that our proposed approach is effective in improving the posture control and stability of knee-wheeled WLRs, particularly in rough terrain conditions.

Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the lateral dynamic control of a vehicle in scenarios where both tracks and wheels are in contact with the ground. A dynamic model of a vehicle is first established based on the tire brush model and linearized general track model. Based on the dynamic model, a novel adaptive model predictive control (AMPC) method is designed considering the coupling and nonlinearity of the wheels and tracks to simultaneously regulate both mechanisms. Compared with traditional model predictive control approaches, the AMPC controller takes the side-slip angle and slip ratio as constraints to prevent the vehicle from reaching unstable states. Simulations are conducted to validate the effectiveness of the controller, and the results indicate that the controller has the capacity to optimize the objective’s yaw-rate response while maintaining lateral vehicle stability and preventing slip by imposing constraints.

We propose Spherical Hierarchical modeling of 3D point cloud. Inspired by Shape Context, we design a receptive field on each 3D point by placing a spherical coordinate on it. We sample points using the furthest point method and creating overlapping balls of points. We divide the space into radial, polar angular, and azimuthal angular bins on which we form a Spherical Hierarchy for each ball. We apply 1x1 CNN convolution on points to start the initial feature extraction. Repeated 3D CNN and max-pooling over the Spherical bins propagate contextual information until all the information is condensed in the center bin. Extensive experiments on five datasets strongly evidence that our method outperforms current models on various Point Cloud Learning tasks, including 2D/3D shape classification, 3D part segmentation, and 3D semantic segmentation.

The suspension system is vital to vehicle performance because it undertakes most of the interactions between wheels and the vehicle body. Due to the significant geometric nonlinearity, there is still a gap of suitable suspension models that are both accurate and computationally efficient. To solve the problem, this paper proposes an explicit solution to the nonlinear geometry of double wishbone suspension by decoupling steering and wheel jumping degrees of freedom (DOF). By discarding the small displacement assumption in the derivation process, the new model gets rid of repeated numerical iterations, resulting in substantial enhancement in computational efficiency. Furthermore, it is noticed in the comparative study that the proposed model can achieve the same level of accuracy as Adams. Benefiting from high computational efficiency and accuracy, the decoupling model presented is successfully used in the optimal design of a double wishbone suspension for smaller variation ranges of wheel alignment parameters. It is anticipated that the research will make significant contribution to fast dimension design of suspension geometry and real-time control of active variable geometry suspensions.

Existing research into the spinal morphology of quadruped robots predominantly simplifies it to a single degree of freedom (DOF) structure, a simplification that is valid for most locomotion gaits, but not optimally suited for a galloping gait where complex spinal movements could confer benefits. Observations of running cheetahs show that the elevation of the hips on the opposing sides of the torso is often uneven, suggesting a potential role of spinal torsion in locomotion. In this paper, a three DOF spine morphology is proposed, in which the front and rear torso have relative pitching DOF in the sagittal plane, while each has its own DOF in a rolling motion. Dynamic models are established for the front and rear torso separately, taking the rigid connection of the torso as constraints, and then motion planning is executed based on trajectory optimization. We adopt a galloping gait and compare this spinal morphology with the rigid torso and single DOF spinal morphology during the locomotion's rapid acceleration, constant high-speed running, and rapid deceleration phases. The results indicate that the DOF of the spine's pitch motion increases the stride between the front and rear legs, while the DOF of the rolling motion increases the stride between the left and right legs. It can be inferred that when the pitch joint angle of the spine is limited, the stride length can be further improved by adding the roll motion joints.