Autonomous vehicles have been widely equipped with radars, camera, and LiDARs due to their complementary capabilities of environment perception. However, it becomes a critical task to accurately track the trajectory of the preceding vehicle when the host vehicle suffers a LiDAR failure under challenging lighting conditions. This article proposes an integrated learning-based solution to address this critical issue. It is composed of a Q -learning-based Gaussian mixture model (QLGMM) for clustering dense radar data, a weight-scheduled method for radar data association, and a switchable recurrent neural network with dual-level long short-term memory (LSTM) cells for trajectory tracking under LiDAR failure and various illumination levels. Specifically, the QLGMM is first introduced to improve the conventional GMM-EM algorithm with the cluster number determined by a Q -learning approach. Then, the weight-scheduled method is presented to associate the data from multiple radars. Furthermore, a switchable dual-level LSTM network is developed to adaptively fuse the trajectories from the radar and camera streams based on three lighting modes. The training data and testing data were acquired on a fully instrumented autonomous vehicle. Experimental verification demonstrates that the proposed method can achieve a promising improvement for LiDAR-fault-tolerant trajectory tracking under different lighting conditions.

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.