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In this study, a finite‐time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long‐time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton‐Jacobi‐Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi‐global practical finite‐time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model. [ABSTRACT FROM AUTHOR]

Physical characteristics of terrains, such as softness and friction, provide essential information for legged robots to avoid non-geometric obstacles, like mires and slippery stones, in the wild. The perception of such characteristics often relies on tactile perception and vision prediction. Although tactile perception is more accurate, it is limited to close-range use; by contrast, establishing a supervised or self-supervised contactless prediction system using computer vision requires adequate labeled data and lacks the ability to adapt to the dynamic environment. In this paper, we simulate the behavior of animals and propose an unsupervised learning framework for legged robots to learn the physical characteristics of terrains, which is the first report to manage it online, incrementally and with the ability to solve cognitive conflicts. The proposed scheme allows robots to interact with the environment and adjust their cognition in real time, therefore endowing robots with the adaptation ability. Indoor and outdoor experiments on a hexapod robot are carried out to show that the robot can extract tactile and visual features of terrains to create cognitive networks independently; an associative layer between visual and tactile features is created during the robot's exploration; with the layer, the robot can autonomously generate a physical segmentation model of terrains and solve cognitive conflicts in an ever-changing environment, facilitating its safe navigation. [ABSTRACT FROM AUTHOR]

In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov–Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic programming-based online adaptive tracking control algorithm is proposed to solve the tracking problem of the partial uncertain time-delayed nonlinear affine system with uncertain resistance. Using the discrete-time Hamilton–Jacobi–Bellman function, the input time-delay separation lemma, and the Lyapunov–Krasovskii functionals, the partial state and input time delay can be determined. With the approximation of the action and critic, and resistance neural networks, a near-optimal controller and appropriate adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the target system, and the tracking error convergence to a small compact set to zero. A numerical simulation of the wheeled mobile robotic system is presented to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Resistance coefficient (RC) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as a constant that depends on the wheel load, wheel dimensions, and soil that the WMR is designed for. This article proposes a novel variable RC that responds to terrain deformation. This variable RC is then applied to controllers for WMRs that estimate driving torques and slip ratios on deformable terrain. Simple yet accurate models of RC are developed from both experimental results and theoretical analysis, and these models are then compared with other methods. The proposed RC models give more accurate and more computationally efficient estimations of driving torques and slip ratios for WMRs, with average estimation errors less than 6% and the shortest computation time in experiments. The two proposed estimators are then applied to the design of the tracking-control systems for a WMR running on deformable terrain. Experiments with simulated sandy terrain demonstrate that both proposed control systems are feasible, and the slip estimation effectively decreases velocity tracking errors from more than 20% to less than 10%. [ABSTRACT FROM AUTHOR]

The increasing application of wheeled mobile robots (WMR) in many fields has brought new challenges on its control and teleoperation, two of which are induced by contact slippage phenomenon between wheel and terrain as well as time delays in the master-slave communication channel. In the WMR bilateral tele-driving system, in this paper, the linear velocity of the slave mobile robot follows the position command from the haptic master robot while the slippage-induced velocity error is fed back as a haptic force felt by the human operator. To cope with the slippage-induced nonpassivity and constant time delays, this paper proposes three methods to design the WMR bilateral teleoperation system’s controller. An experiment system is set up with Phantom Premium 1.5A haptic device as the master robot and a simulation platform of WMR as the slave robot. Experiments with the proposed methods demonstrate that they can result in a stable WMR bilateral tele-driving system under wheel’s slippage and constant time-delays. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMRs) in real complex environments such as on extraterrestrial planets are confronted with uncertain external disturbances and strong coupling of wheel-ground interactions while tracking commanded trajectories. Methods based on sliding mode control (SMC) are popular approaches for these situations. Traditional SMC has some potential problems, such as slow convergence, poor robustness, and excessive output chattering. In this paper, a kinematic-based feed-forward control model is designed for WMRs with longitudinal slippage and applied to the closed-loop control system for active compensation of time-varying slip rates. And a new adaptive SMC method is proposed to guide a WMR in trajectory tracking missions based on the kinematic model of a general WMR. This method combines the adaptive control method and a fast double-power reaching law with the SMC method. A complete control loop with active slip compensation and adaptive SMC is thus established. Simulation results show that the proposed method can greatly suppress chattering and improve the robustness of trajectory tracking. The feasibility of the proposed method in the real world is demonstrated by experiments with a skid-steered WMR on the loose-soil terrain. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed. [ABSTRACT FROM AUTHOR]

The lunar surface is a complicated and unknown terrain, and hence, astronauts must be trained to travel on that surface by using a prototype vehicle on the Earth's terrain. However, the gravity of the Earth is six times that of the Moon; thus, the acceleration of a prototype vehicle on the Earth will differ from that of a manned lunar rover on the Moon, when the same acceleration handling input is provided. To obtain the same acceleration in the prototype vehicle on the Earth as that obtained on the Moon, an imitation handling ratio describing the relationship between the acceleration handling input and the wheel rolling speed is proposed in order to replicate the lunar gravitational conditions on the Earth. First, a whole-vehicle acceleration model was established based on the mechanical model of a deformable wheel with a mesh surface. Thereby an acceleration imitation algorithm was derived. A prototype vehicle for use on the Earth is proposed for the purpose of imitating the manned lunar rover under four different vehicle conditions. Fitted functions were used in order to obtain the imitation handling ratio. Finally, experiments were conducted to verify the imitation method; the results revealed that the prototype vehicle on the Earth could effectively approximate the same accelerations experienced by a manned lunar rover, when equivalent acceleration handling inputs were provided. [ABSTRACT FROM AUTHOR]

For motion control of wheeled planetary rovers traversing on deformable terrain, real-time terrain parameter estimation is critical in modeling the wheel-terrain interaction and compensating the effect of wheel slipping. A multi-mode real-time estimation method is proposed in this paper to achieve accurate terrain parameter estimation. The proposed method is composed of an inner layer for real-time filtering and an outer layer for online update. In the inner layer, sinkage exponent and internal frictional angle, which have higher sensitivity than that of the other terrain parameters to wheel-terrain interaction forces, are estimated in real time by using an adaptive robust extended Kalman filter (AREKF), whereas the other parameters are fixed with nominal values. The inner layer result can help synthesize the current wheel-terrain contact forces with adequate precision, but has limited prediction capability for time-variable wheel slipping. To improve estimation accuracy of the result from the inner layer, an outer layer based on recursive Gauss-Newton (RGN) algorithm is introduced to refine the result of real-time filtering according to the innovation contained in the history data. With the two-layer structure, the proposed method can work in three fundamental estimation modes: EKF, REKF and RGN, making the method applicable for flat, rough and non-uniform terrains. Simulations have demonstrated the effectiveness of the proposed method under three terrain types, showing the advantages of introducing the two-layer structure. [ABSTRACT FROM AUTHOR]

To track the desired trajectories of the wheeled mobile robot (WMR) with time-varying forward direction, a reinforcement learning-based adaptive neural tracking algorithm is proposed for the nonlinear discrete-time (DT) dynamic system of the WMR with skidding and slipping. And, the typical model is transformed into an affine nonlinear DT system, the constraint of the coupling robot input torque is extended to pseudo dead zone (PDZ) control input. Three neural networks (NNs) are introduced as action NNs to approximate the unknown modeling item, the skidding and the slipping item and the PDZ item, whereas another NN is employed as critic NN to approximate the strategy utility function. Then, the critic and action NN adaptive laws are designed through the standard gradient-based adaptation method. The uniform ultimate boundedness (UUB) of all signals in the affine nonlinear DT WMR system can be ensured, while the tracking error converging to a small compact set by zero. Numerical simulations are conduced to validate the proposed method. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMR) have been deployed in Mars/Lunar exploration, military missions, and geological investigations, and they must handle rough and deformable terrains. The dynamics at the wheel–soil interface, including the wheel–terrain contact angle, wheel sinkage, and wheel–soil interaction forces and torque, are major factors in the entrapment of NASA SPIRIT MER and the subsequent mission failure. This paper presents an experimental apparatus that is developed to measure the wheel–terrain contact angle, wheel sinkage, and wheel–soil interaction forces and torque in real time to improve our understanding of the WMR mobility on soft and deformable terrains. A method for wheel sinkage evaluation is presented based on measuring the wheel–terrain contact angle. The wheel–soil interaction forces and torque are measured by installing a force/torque sensor along the axle of the apparatus. The proposed approach is implemented on a testbed, and the experimental results are used to evaluate it; they demonstrate the reliable detection of the wheel–terrain contact angle, wheel sinkage, and wheel–soil interaction forces and torque. Moreover, based on the measured wheel–terrain contact angle, the relative errors in the predicted wheel–soil interaction forces and torque are less than 8% compared with the experimental data. [ABSTRACT FROM PUBLISHER]

To successfully deploy a wheeled mobile robot on deformable rough terrains, the wheel-terrain interaction mechanics should be considered. Skid terramechanics is an essential part of the wheel terramechanics and has been studied by the authors based on the wheel sinkage obtained using a linear displacement sensor that does not consider soil bulldozing effect. The sinkage measured by a newly developed wheel via detecting the entrance angle is about 2 times of that measured by the linear displacement sensor. On the basis of the wheel sinkage that takes the soil bulldozing effect into account, a linear function is proposed to the sinkage exponent. Soil flow in the rear region of wheel-soil interface is considered in the calculation of soil shear displacement, and its average velocity is assumed to be equal to the tangential velocity component of the transition point of shear stress. To compute the normal stress in the rear region directly, the connection of the entrance and leaving points is supposed as the reference of wheel sinkage. The wheel performance can be accurately estimated using the proposed model by comparing the simulation results against the experimental data obtained using two wheels and on two types of sands. [ABSTRACT FROM AUTHOR]

The success of NASA's Mars missions has generated a renewed interest in planetary exploration, along with a need for better mobility of wheeled mobile robots (WMRs)—steering performance, in particular, has not yet reached an acceptable benchmark in high-precision models. This study focuses on the steering mechanics of WMRs, as it can enrich the terramechanics of planetary rovers. An improved sinkage model based on the variable sinkage exponent is proposed, and the sinkage exponent is derived as a function of steering angle. Furthermore, a high-precision steering moment model is proposed based on the steering-sinkage model, which considers the interaction area and the lug effect. The model can fit the results obtained by all of the experimental wheels, based on soil parameter identification. Most of the max relative errors ( MRE s) for the sinkage are within 5%, and the coefficients of determination are more than 0.97. The MRE s of moment increase with increasing vertical load but remain within 10%. This technique can also be applied to other WMRs with rigid wheels steering on deformable terrain. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN)-based tracking control algorithm is proposed for the wheeled mobile robotic (WMR) system with full state constraints. It is the first time to design an adaptive NN-based control algorithm for the dynamic WMR system with full state constraints. The constraints come from the limitations of the wheels’ forward speed and steering angular velocity, which depends on the motors’ driving performance. By employing adaptive NNs and a barrier Lyapunov function with error variables, then, the unknown functions in the systems are estimated, and the constraints are not violated. Based on the assumptions and lemmas given in this paper and the references, while the design and the system parameters chose properly, our proposed scheme can guarantee the uniform ultimate boundedness for all signals in the WMR system, and the tracking error converge to a bounded compact set to zero. The numerical experiment of a WMR system is presented to illustrate the good performance of the proposed control algorithm. [ABSTRACT FROM PUBLISHER]

Wheeled mobile robots (WMR) are often applied to travel on outdoor unstructured environment, such as loose soil or variable field terrain. Learning the knowledge of terrain has played a significant role for better mobility and stability of WMR. In this study, a diagonal recurrent neural network-based adaptive method is proposed to identify terrain parameters by the platform of a single driving wheel. According to the classical terramechanics model of wheel-soil interaction, a decoupling simplification model is developed by closed-form analytical equations. Five unknown terrain parameters are divided into two groups and included in two complex nonlinear equations. These parameters are used to compute the model outputs of force and torque of wheel-soil interaction. Dynamic back propagation algorithm is applied to update these parameters for compensating the errors between the prediction of neural network and measurable data in real time. The results of simulation show that the terrain parameters can be obtained and approximate the experimental value of terrain parameters when the predictive errors converge to zero. [ABSTRACT FROM AUTHOR]

With the increasing applications of wheeled mobile robots (WMRs) in various fields, some new challenges have arisen on designing its teleoperation system. One of such challenges is caused by wheel's slippage. This paper proposes a new approach for haptic teledriving of a WMR coupled with slippage. In this teleoperation system, the WMR's linear velocity and angular velocity, respectively, follow the master haptic interface's positions. The proposed teleoperation controller also includes an acceleration-level control law for the WMR so that the WMR's linear and angular velocity loss induced by the slippage can be compensated for. Information caused by wheel's slippage in the environment termination (ET) is displayed to the human operator through haptic (force) feedback. After designing a local controller to compensate for the ET's nonpassivity caused by the slippage, the system's stability is shown via its passivity and it is also shown that the force felt by the human operator is approximately equal to the output force of the ET. Experiments of the proposed controller demonstrate that the modified ET is passive and the controller can result in stable bilateral teleoperation with a satisfactory tracking performance. [ABSTRACT FROM PUBLISHER]

This paper presents the design, optimization and simulation experimental evaluation of a novel crawling–swimming hexapod robot mobile mechanism for coral forest scientific research in the South China Sea. The robot has two modes of seafloor crawling and deep-sea cruising, and it also has two-sided crawling capability. In this paper, a leg structure size optimization model is established in detail, and the single leg can meet the obstacle crossing index after optimizing the size with the help of genetic algorithm. A single-leg dynamics model of legged robot including foot–terrain interaction mechanics and hydrodynamics is proposed in this paper, and an underwater dynamics simulation platform based on Vortex is built on this basis. The experimental results of seafloor crawling simulation show that the robot can steadily crawl up a 20°slope under the condition of 0.15 m/s frontal impact of subsea current velocity. In addition, the simulation results of subsea walking stability show that the stability of tripod, tetrapod and wave gaits gradually increases, but the walking speed gradually decreases. This study provides an important support for the prototype development and underwater gait selection of crawling–swimming hexapod robot. • The underwater hexapod robot that can both crawl and swim under the sea is designed. • A novel leg structure size optimization model considering the underwater environment. • The single-leg underwater dynamic model considering foot–terrain interaction mechanics is established. • A simulation platform for underwater crawling dynamics of the hexapod robot is built based on Vortex. [ABSTRACT FROM AUTHOR]

In this paper, a robust model predictive control (MPC) scheme using neural network-based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state-scaling transformation, the intrinsic controllability of the mobile robot can be regained by incorporation into the control input $u_1$ an additional exponential decaying term. An MPC-based control method is then designed for the robot in the presence of external disturbances. The MPC optimization can be formulated as a convex nonlinear minimization problem and a primal–dual neural network is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been improved by the proposed neurodynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach. [ABSTRACT FROM PUBLISHER]

Deformable slope is a type of terrain that wheeled mobile robots (WMRs) and ground unmanned vehicles (GUVs) may have to traverse to accomplish their mission tasks. However, the associated terramechanics for wheels with arbitrary posture is rarely studied. In this paper, based on wheel attitude, dynamics of the wheel–terrain interaction for a rigid wheel on deformable slope is investigated. Through introducing the angular geometry of wheel attitude into terramechanics theory, a generalized dynamic model is developed, involving two inclination angles of slope and three attitude angles of wheel steering axis. Two representative cases are studied: the wheel runs straight forward and perpendicular to the slope, and the wheel is in a steering maneuver with an inclined steering axis. A generalized kinematic model for wheel–terrain contact point and wheel center is also provided, which analytically explicates that trajectory of wheel motion is coupled with wheel attitude while driven by angular rates. The proposed attitude-based models are valid for arbitrary wheel–terrain geometry and can lead to control purpose directly. Effectiveness of the models is confirmed by simulating the influences from attitude to wheel mechanics and motion. [ABSTRACT FROM AUTHOR]

With the widespread use of wheeled mobile robots (WMRs) in various applications, new challenges have arisen in terms of designing its control system. One of such challenges is caused by wheel slippage. This study proposes a new method for haptic teleoperation control of a WMR with longitudinal slippage (not including sliding). In this teleoperation system, the mobile robot's linear velocity follows the master haptic interface's position. The proposed teleoperation controller also includes an acceleration‐level control law for the mobile robot such that the velocity loss caused by slippage is compensated for. Information about the magnitude and timing of slippage is displayed to the human operator through haptic (force) feedback. Despite the functional benefits of displaying slippage information as haptic feedback to the user, there are system stability related concerns that have been addressed using the proposed controller. Experiments of the proposed controller demonstrate that it results in stable bilateral teleoperation with a satisfactory tracking performance. [ABSTRACT FROM AUTHOR]

As a major representative nonholonomic system, wheeled mobile robot (WMR) is often used to travel across off-road environments that could be unstructured environments. Slippage often occurs when WMR moves in slopes or uneven terrain, and the slippage generates large accumulated position errors in the vehicle, compared with conventional wheeled mobile robots. An estimation of the wheel slip ratio is essential to improve the accuracy of locomotion control. In this paper, we propose an improved adaptive controller to allow WMR to track the desired trajectory under unknown longitudinal slip, where the stabilisation of the closed-loop tracking system is guaranteed by the Lyapunov theory. All system states use neural network online weight tuning algorithms, which ensure small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed control method in various Matlab simulations. [ABSTRACT FROM AUTHOR]

Due to the requirements of challenging planetary exploration missions with wheeled mobile robots (WMRs), the driving mechanics of WMRs’ wheels moving on the deformable terrain has been researched intensively, but the mechanics of the wheels’ steering is lacking research. Systematic steering experiments were carried out using a single-wheel testbed for wheels moving on a lunar soil simulant with different radii, widths, lug heights, and lug numbers under different vertical loads. The influence of the eccentric distance and motion state, such as the steering motor’s angular velocity, steering angle, and initial wheel sinkage, were also studied. The experimental results are illustrated with plenty of figures and analyzed based on the preliminary steering mechanics model to draw conclusions. The steering resistance moment is caused by the lateral bulldoze stress and the shearing stress at the bottom of the wheel. The wheel sinkage and steering moment of resistance increase with an increase in steering angle, which could be fitted with exponential functions. The steering moment is the increasing function of the wheel sinkage, eccentric distance, vertical load, and wheel width. The conclusions, empirical models, and experimental data can be taken as references to the optimal design of a steering mechanism and the development/verification of a wheel’s steering mechanics model. [ABSTRACT FROM AUTHOR]

• Finish foot-terrain interaction experiments under large sinkage and dynamic sliding. • Establish mechanical models considering large sinkage and dynamic sliding effect. • Design a coronal foot with better anti-sinkage and tangential traction performance. Legged robots exhibit superior adaptability to complex extraterrestrial environments compared to wheeled mobile robots. However, legged robots employed in planetary exploration face challenges in dealing with soft terrains. This paper focuses on investigating the issues of large foot sinkage and slip encountered by legged robots on soft terrain. Extensive experiments on quasi-static loading, loading with impact and tangential force have been carried out for both spherical and cylindrical feet. The variations in normal force, tangential force, and sinkage are meticulously recorded and analyzed. Foot-terrain interaction mechanics models are established to address scenarios involving substantial sinkage and sliding sinkage, leveraging the stress distribution characteristics of deformable soil. Accurate models are obtained through parameter identification utilizing experimental data, which can aid in the foot design of legged robots intended for planetary exploration. Based on the developed models and experimental data, a design optimization scheme for the coronal foot is proposed, leading to performance enhancements that are validated through experimental verification. [ABSTRACT FROM AUTHOR]

An adaptive neural network-based finite-time tracking control method is proposed for wheeled mobile robots (WMR) in the presence of the slipping with full state time-varying constraints. The practical WMR model with coupled inputs as two torques for each wheel is difficult for the designed controllers to achieve finite-time convergence. A novel adaptive finite-time tracking control strategy is proposed by satisfying the time-varying constraints and utilizing barrier Lyapunov functions. It is proved that the controllers we designed are finite-time stable, and each tracking error converges to a small neighborhood of the original states in a finite time without violation of the full state constraints. Finally, a simulation based on a practical WMR model is conducted to verify the effectiveness of the proposed tracking control method. By using the exponential-decayed time-varying constraints, the simulation results show that the tracking control effect is very optimistic, which means that the control method proposed in this paper has great significance in engineering. [ABSTRACT FROM AUTHOR]

Highlights • Theoretical and experimental research was done for online terrain parameters and resistance force estimation. • Both normal and shear stresses can be expressed linearly using equivalent wheel sinkage. • Terrain's mechanical properties are re-characterized by proposed stiffness and shear strength. • Maximum relative error of the estimated resistance force is less than 7% at steady state. Abstract Wheel-soil interaction mechanics plays a crucial role for wheeled mobile robots (WMR) on rough and deformable terrains such as Martian and Lunar surfaces. Skid terramechanics is an essential component for WMRs and generates resistance force when a WMR brakes or on downhill slopes. The basis of classical terramechanics theories for WMRs – Bekker's normal stress and Janosi's shear stress equations – are so complex that the wheel-soil interaction force/torque equations are not amenable to closed form solutions, which seriously limits the application of terramechanics theories to WMRs. To establish analytical wheel-soil interaction expressions, the normal and shear stresses that can be characterized linearly by the proposed terrain stiffness and shear strength, respectively, are presented in this paper. Terrain stiffness and shear strength can be used to characterize terrain mechanical properties. Compared with the experimental data, the maximum relative error of the resistance forces estimated using these expressions at steady state is less than 7%. These validated expressions can be applied to estimate terrain parameters and resistance force online with high accuracy. Terrain's stiffness and shear strength increase first, and then reach a constant. Before wheels entering steady state, the online estimated resistance force's relative error is much higher, which can be explained using wheel's vertical velocity. [ABSTRACT FROM AUTHOR]

With the increasing application of wheeled mobile robots on soft terrains, the challenge of lateral and longitudinal slippage existing in the contact surface between the wheels and the terrain has attracted more attention. To address the difficulties caused by the lateral and longitudinal slippage, this paper proposes an improved linear active disturbance rejection control (LADRC) method for path tracking control of a six-wheeled corner steering rover. Based on the LADRC, the tracking differentiator and nonlinear state error feedback are introduced into the improved LADRC. By using the improved LADRC, the influence of disturbances in inputs can be attenuated and a higher regulating efficiency than LADRC can be achieved. The simulations validate the effectiveness of the proposed approach with a good tracking performance. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots are often used on high risk rough terrain. Sandy terrains are widely distributed and tough to traverse. To successfully deploy a robot in sandy environment, wheel-terrain interaction mechanics in skid should be considered. The normal and shear stress is the basis of wheel-soil interaction modeling, but the normal stress in the rear region on the contact surface is computed through symmetry in classical terramechanics equations. To calculate that directly, a new reference of wheel sinkage is proposed. Based on the new reference, both the wheel sinakge and the normal stress can be given using a quadratic equation as the function of wheel-soil contact angle. Moreover, the normal stress can be expressed as a linear function of the wheel sinkage by introducing a constant coefficient named as sand stiffness in this paper. The linearity is demonstrated by the experimental data obtained using two wheels and on two types of sands. The sand stiffness can be estimated with high accuracy and it decreases with the increase of skid ratio due to the skid-sinkage phenomenon, but increases with the increase of vertical load. Furthermore, the sand stiffness can be utilized directly to compare the stiffness of various sandy terrains. [ABSTRACT FROM AUTHOR]

Wheeled mobile robot (WMR)-based exploration on soft terrains has created new difficulties for its tele-control system. A critical problem is that wheel-terrain contact areas may induce both longitudinal slipping and lateral sliding. In this paper, a new approach for tele-driving a WMR that addresses the difficulties simultaneously induced by both longitudinal slipping and lateral sliding is presented. In the augmented coordination between the master and slave site, the translational and angular velocities of the slave WMR were modified by the wheel sliding angle. Moreover, they were separately coordinated with the positions of the master haptic interface. To improve the command-tracking performance with the augmented coordination, the teleoperator was designed with feedforward of the sliding angle. To compensate for the non-passivity induced by longitudinal slipping and lateral sliding, a time-domain passivity controller also is proposed. The stability and transparency of the proposed approach were analyzed based on the passivity theory. Numerical experiments validate that this proposed approach can effectively improve the teleoperation performance of human operators for the WMR on soft terrains. [ABSTRACT FROM AUTHOR]

• A neural network control system for PDW active following control is proposed. • An online sequential forgetting update method for OS-ELM networks is presented. • An efficient and highly performant hybrid OS-ELM-PID control system is developed • The proposed hybrid controller handles terrain deformation disturbances efficiently. The performance of the wheel-driving control scheme in wheeled mobile robots is a vital aspect in vehicular robotics applications, both for fully exerting the robot tractive capabilities and to save energy. This is especially true in contexts in which the robot must travel through unknown and unpredictable deformable terrains, such as the planetary exploration context considered in this study. To compensate the disturbances resulting from terrain deformations while simultaneously leveraging the control advantages provided by the use of the recently proposed concept of pseudo-driven wheels (PDWs), an artificial-neural-network-based control method is proposed here. This study develops and presents the network algorithms necessary for achieving active following control on velocity tracking for PDWs. To handle the considered complex and highly uncertain wheel-terrain interactions, an online sequential forgetting update method for the neural network is presented, and an improved online sequential extreme learning machine (OS-ELM), combined with a proportional integral derivative (PID) controller is used, thereby leading to an efficient and highly performant hybrid OS-ELM-PID control system. A simulation showed the feasibility of the proposed control method, and subsequent real-life experimental results demonstrate the capability of the control system in maintaining the drawbar force on the PDW within the range F DP = ± 2 N. [ABSTRACT FROM AUTHOR]

• Highly complex wheel-terrain contact mechanics theories can be simplified into closed-form analytical models. • Terrain parameters can be estimated in-situ using wheels rather than specially developed apparatus. • Wheel sinkage and drawbar pull can be explicitly estimated in-situ using wheel-terrain interaction forces and torque. • Single wheel experiments with two wheels and two regolith simulants verify the in-situ applications' effectiveness. Physical space exploration missions usually include exploring craters that are composed of various slopes and covered with fine regolith. Wheeled mobile robots (WMRs), commonly known as rovers, are widely used in these missions to find a host of information on topography, mineralogy, geological processes, etc. In traditional terramechanics theories, at least seven experiments under various slip ratios are required to estimate the terrain parameters and drawbar pull that determines a WMR's slope climbing capability. Moreover, the wheel sinkage—an important index that indicates whether the fine and soft planetary regolith would become a dangerous trap—cannot be estimated in-situ currently. In this paper, highly coupled terramechanics equations are simplified into closed-form analytical models, whose effectiveness is experimentally validated after wheels enter quasi-static conditions. Based on these validated equations, the goal of in-situ estimation of terrain parameters, drawbar pull, and wheel sinkage can be accomplished by directly measuring physically measureable quantities. Thus, wheeled rovers will have the ability to perceive the planetary regolith's mechanical properties and the wheels' running condition by mounting an instrumental wheel that contains a rotation force/torque sensor. [ABSTRACT FROM AUTHOR]

• Negative effects of wheel slippage on kinematic model of SWMR are revealed. • A new two-level architecture for teleoperation of SWMR is proposed. • Teleoperators with local command compensation controllers are proposed. • Experiments demonstrate the proposed controllers can result in good performance. With the increasing use of multi-wheeled mobile robots in various fields, a few new challenges pertaining to the design of the teleoperation system of such robots have emerged. This paper proposes a new two-level architecture for haptic teleoperation of a six-wheeled mobile robot (SWMR) with a rocker-bogie chassis on soft terrains. In teleoperation, the linear and angular velocities of the SWMR base follow the master robot's positions. At the slave SWMR level, local compensation controllers are designed for the driving motors and the steering motors to track the desired velocities of the base and eliminate the negative influence of wheel slippage. Command-tracking errors caused by wheel slippage at environment termination (ET) are conveyed to the human operator through haptic (force) feedback. By guaranteeing the passivity of ET, the stability conditions of the teleoperation system are constrained by the Llewellyn criterion. Experiments conducted using the proposed controllers demonstrated that the tracking errors in the commands sent to the slave robot are effectively compensated for, and the controllers achieve stable teleoperation with satisfactory tracking performance. [ABSTRACT FROM AUTHOR]