The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang’E-4 mission successfully targeted the Moon’s farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range.

Considering the uncertain nonstrict nonlinear system with dead-zone input, an adaptive neural network (NN)-based finite-time online optimal tracking control algorithm is proposed. By using the tracking errors and the Lipschitz linearized desired tracking function as the new state vector, an extended system is present. Then, a novel Hamilton-Jacobi-Bellman (HJB) function is defined to associate with the nonquadratic performance function. Further, the upper limit of integration is selected as the finite-time convergence time, in which the dead-zone input is considered. In addition, the Bellman error function can be obtained from the Hamiltonian function. Then, the adaptations of the critic and action NN are updated by using the gradient descent method on the Bellman error function. The semiglobal practical finite-time stability (SGPFS) is guaranteed, and the tracking errors convergence to a compact set by zero in a finite time.

In this paper, a dynamic model of a wheeled mobile robotic (WMR) system with coupled control input is developed, which will increase the complexity of its tracking control with time-varying advance angle. To deal with this problem, a partial reinforcement learning neural network (PRLNN)-based tracking algorithm is proposed for the WMR systems. The main contributions of the PRLNN adaptive tracking control method is that it is the first control method to introduce the PRLNN adaptive control to the WMR system, which determines to solve the WMR tracking control with the time-varying advance angle. The critic neural network (NN) and action NN adaptive laws for the decoupled controllers are designed using the standard gradient-based adaptation method. According to the Lyapunov stability analysis theorem, the uniform ultimate boundedness of all signals in the WMR system can be guaranteed with the design parameters chose properly, and the tracking error converge to a small compact set nearby zero. A numerical simulation is presented to verify the effectiveness of the proposed control algorithm.

Wheeled mobile robots (WMRs) with variable wheelbases are capable of traveling on deformable terrains and handling complex detection tasks. While the variable wheelbase length of WMR allows it to interact with the terrains adaptively, enhancing its mobility, it brings a control challenge. Inspired by the worm's movement of stretching body at different lengths under different environmental resistance, a creeping gait (CG) strategy is proposed in this work to enable the WMR to be controlled in dual modes: wheeled following mode (WFM) and specified length mode (SLM). WFM adjusts the wheelbase's length by the wheels' movements freely to minimize the internal force and torque between wheels. SLM adjusts the wheelbase's length using a proposed fuzzy logic based algorithm to stabilize the body's posture on rough terrain and overcome specific motion challenges, like escaping wheel sinking. A state-adaptive mode-switching controller is then developed using the dwell time approach to smooth the output velocities during the switching phase, and a Lyapunov analysis is performed to verify its stability. According to the results of physical experiments, three-wheeled mobile robot movements with CG enable more precise path following by 37% and faster response by 11% compared to fixed wheelbase movements, and the dwell time approach achieves smoother speed transitions between the modes than the direct switching method, especially when moving from flat to slope terrain.