In this article, a novel Lyapunov-based event-trigger mechanism is proposed to reduce the computation cost of model predictive control (MPC) algorithm for discrete-time nonlinear input-affine safety-critical systems. Unlike conventional approaches that require continuous error monitoring, the proposed mechanism leverages the predictive capability of MPC to determine triggering instants directly based on the evolution of the closed-loop Lyapunov function. Safety and stability are enforced by incorporating control barrier functions (CBFs) and control Lyapunov functions (CLFs) as constraints within the MPC optimization. Furthermore, the recursive feasibility of the proposed event-triggered MPC algorithm is rigorously analyzed, with special attention to the potential infeasibility caused by hard CBF constraints. Input-to-state practical stability (ISpS) of the resulting closed-loop system is also established. Simulation results demonstrate that the proposed event-triggered CBF-CLF-MPC algorithm effectively eliminates unnecessary controller updates, reducing computational consumption while maintaining tracking performance comparable to that of a conventional time-triggered MPC algorithm.

Multi-Objective evolutionary algorithms (MOEAs) are popular for solving complex optimization problems with multiple objectives and they have gained much success in benchmark competitions as well as real-world applications. Most MOEAs are effective for specific types of problems, leading to a lack of generality. This paper presents a dynamic MOEA employing a learning strategy to adaptively allocate the appropriate optimizer for the given problem. Three key issues are considered by the designed learning strategy, including environmental information collection, information-reward conversion, and dynamic method selection. In the dynamic selection process, a set of candidate algorithms with various features is required, which improves the universality of our method. Extensive experimental comparisons have been done based on the benchmark functions WFG1–WFG9, as well as a real-world problem, which shows that the overall performance of the proposed method is superior to the state-of-the-art peer algorithms.

The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable navigation mode, composing of perception, mapping and planning modules designed for different terrain types, to traverse the terrain ahead before reaching the next waypoint. By integrating the local navigation system with a map server and a global waypoint generation module, the rover is equipped to handle long-distance navigation tasks in complex scenarios. The navigation system is evaluated in various simulation environments. Compared to the single-mode conservative navigation method, our multi-mode system is able to bootstrap the time and energy efficiency in a long-distance traversal with varied type of obstacles, enhancing efficiency by 79.5%, while maintaining its avoidance capabilities against terrain hazards to guarantee rover safety. More system information is shown at https://chengsn1234.github.io/multi-mode-planetary-navigation/.

Considering the increasingly widespread use of multilegged robots in various fields, the design of their bilateral or multilateral teleoperation system is facing new challenges, such as foot slippage, tele-driving skills, and operators experience, which can induce activity of its teleoperation and poor maneuverability. With the assistance of an experienced trainer and the development of a multi-user cooperative teleoperation system, it is more conducive to tele-driving the multilegged robot in real field environment. Accordingly, in this study, a novel trilateral shared control architecture based on the velocity and force coordination of a dual-user haptic tele-training system for a hexapod robot subjected to soft terrain is proposed. Its methodology simultaneously considers the online transfer strategy of the task authority adjustment between dual users and the dynamic compensation approach for the active exogenous disturbance. In our system, the dominance factor adjusts the control authority of dual users according to the velocity and force command-tracking performance of the trilateral system. The closed-loop stability of the system is established by its passivity, and the force applied by the environment termination is determined to be approximately equal to the sum of the forces felt by two operators. The experiments of the proposed dual-user haptic training system for the hexapod robot demonstrate that it results in a stable trilateral teleoperation with a satisfactory tracking performance. Note to Practitioners—Multi-legged mobile robots have been widely used in daily and scientific scenarios, including industrial manufacturing, field exploration, and domestic service. However, as tasks and requirements become larger and more complex, the difficulty of development increases. To overcome these issues, this paper proposes a trilateral tele-training architecture for teleoperating the movement of a hexapod robot on deformable terrain, with complications such as foot slippage. The shared control method incorporates primarily two novel technical contributions, which refer to an adaptive authority adjustment method to allocate control between trainer and trainee operators, and a passivity controller (an adaptive damper) informed by observed loss in the remote robot’s velocity (both of which can be executed simultaneously). The former is implemented with a varied dominance term. Two different shared control methods are proposed, one which essentially relies on the errors in provided position inputs between the two leaders and the follower (position-error-based, PEB), and one that examines torques applied to the operators compared with environmental interaction force (direct force reflection, DFR). In addition, the practitioners who research in fields where efficient control of cooperative teleoperation for multi-legged robots subject to outdoor environment can benefit from these proposed methods. In future research, we will further address the dynamic teleoperation of hexapod robot and time delays.

Wheeled mobile manipulators (WMMs) involving a wheeled mobile platform and a serial manipulator are finding increasing applications in diverse fields, creating new challenges in performing high-precision operations in a spacious workspace. WMMs are challenging to control due to uncertainties in system parameters, coupled dynamics, and external disturbances, which make stability guarantees difficult. This paper proposes a virtual decomposition control (VDC)-based trajectory tracking controller for WMMs, addressing joint flexibility, external disturbances, etc. The proposed method uses a VDC-based iterative approach to manage the complex coupled dynamics and employs a separate adaptive controller to handle joint flexibility. The robotic system’s stability is validated using the specific features of VDC (proof of each subsystem’s virtual stability) according to the Lyapunov stability theory. The advantages and effectiveness of the proposed method are demonstrated through experiments.Note to Practitioners—This paper addresses the challenges faced in controlling WMMs, which are becoming increasingly common in various industrial and service applications due to their ability to perform tasks in large and dynamic environments. The coupling between the wheeled platform and the manipulator, as well as uncertainties in system parameters such as joint flexibility and external disturbances, make precise trajectory tracking difficult. To address these challenges, this paper presents a control approach based on VDC, which breaks down the complex system into manageable subsystems and ensures stability for each part individually. The control strategy also incorporates adaptive control to handle joint flexibility and unpredictable disturbances. The stability of the system is rigorously proven through Lyapunov theory, ensuring robust performance under real-world conditions. Practitioners working on autonomous mobile robots equipped with manipulators may find this approach useful for improving trajectory tracking performance in uncertain and dynamic environments. However, the practical implementation of this method will require careful tuning of controller parameters and real-time computational capabilities to ensure seamless operation in real applications.

Agile quadruped robots are ideal for performing heavy-load transportation tasks in industrial settings; however, the uncertainty of external disturbances and distribution shifts pose significant challenges to system stability. We propose a complex dynamics modeling method based on the liquid neural operator (LNO), which achieves over 92% accuracy in dynamics prediction across various unknown environments. The high-fidelity dynamics model generated by LNO is controlled by a novel deep model predictive control (DMPC) method, which–to our knowledge–represents the first successful application of Koopman operator theory to real-world dynamic control of legged robots. The DMPC is equivalent to a constrained convex quadratic programming problem through state variable reconstruction. Moreover, we design a deep reinforcement learning-MPC (RL-MPC) hierarchical control architecture based on LNO, where embedding the LNO into RL policy exploration establishes a new paradigm for bridging the sim-to-real gap through operator-theoretic feature learning, enabling the robot to demonstrate high adaptability under changes in load or environment. The proposed method improves the load-to-weight ratio of the Unitree A1 robot to 1.25, and it is capable of withstanding multiple impacts with a 15 kg load. Experimental results demonstrate that the comprehensive load-bearing capacity of the A1 remarkably surpasses the State-of-the-Art achievements.

Multimode motion capability in self-reconfigurable wheeled mobile robots (SRWMRs) can be achieved by designing various control modes and the corresponding switching strategies adapted to different motion objectives. While SRWMR is capable of strong traction and moving at a fast speed by utilizing robot creeping (RC) and wheel rolling (WR) modes, respectively, the autonomous RC mode control is challenging due to the parameter definition of mode-switching control (MSC). Therefore, to obtain the main control parameters in an SRWMR, an ROSTDyn Vortex platform is developed to analyze the traction, slippage, and sinkage variation of the modes on varied soil terrains. To autonomously activate the RC mode while the robot is in WR mode, an MSC method is proposed by utilizing fuzzy logic algorithms, taking into account the slip ratio of wheels and their change in rate. Furthermore, for RC mode, to enhance the motion efficiency by adaptively changing the wheelbase length on different types of soil terrains, the control indices on energy consumption and forward movement are presented with the consideration of the defined slip ratio indices. According to the results of simulation experiments, including climbing slopes and escaping from the wheel sinking on the designed soil terrains with varied sloping degrees and stiffness, the autonomous RC control was effectively achieved by the proposed MSC, leading to body traction enhancement. In addition, a 31% reduction in energy consumption was achieved by RC mode with adjustable parameters, compared to the regular RC control.

Based on actor-critic neural networks (NNs), an optimal controller is proposed for solving the constrained control problem of an affine nonlinear discrete-time system with disturbances. The actor NNs provide the control signals and the critic NNs work as the performance indicators of the controller. By converting the original state constraints into new input constraints and state constraints, the penalty functions are introduced into the cost function, and then the constrained optimal control problem is transformed into an unconstrained one. Further, the relationship between the optimal control input and worst-case disturbance is obtained using the Game theory. With Lyapunov stability theory, the control signals are ensured to be uniformly ultimately bounded (UUB). Finally, the effectiveness of the control algorithms is tested through a numeral simulation using a third-order dynamic system.

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.

This article proposes an adaptive finite-time tracking control based on fuzzy-logic systems (FLSs) for an uncertain nonstrict nonlinear multi-input-multi-output (MIMO) full-state-constrained system with the coupled uncertain dead-zone input. By using three kinds of FLSs: the uncertain system, the uncertain dead zone, and the uncertain input transfer inverse matrix are approximated using the system function FLS, dead-zone FLS, and input transfer inverse matrix FLS, respectively. After defining the barrier Lyapunov function, the fuzzy-based adaptive tracking controllers are designed, and the fuzzy weights are updated through the proposed adaptive laws. Then, based on the extended finite-time convergence theorem, with the design parameters chosen properly, the target uncertain nonlinear system is guaranteed to be semiglobal practical finite-time stable (SGPFS); and the full-state constraints are not violated while avoiding the effects of the dead zones. Furthermore, a simulation is presented to verify the validity of the proposed algorithm.

Planetary rovers may traverse terrains with complex geometries and variable physical properties, but their mobility behaviors are complicated and difficult to simulate precisely. This article focuses on high-fidelity dynamic modeling and simulation for a type of rovers that incorporate single-input-multi-output joints to enhance terrain adaptability, which has been used on China's Tianwen-1 Mars rover. A novel multibody dynamic model and its solutions are derived first with consideration of single-input-multi-output joints. Then, a unified terramechanics model is proposed, considering variable terrain surfaces and covering rover's motion states of skidding, slipping, and steering, solved the problem of simulation instability caused by model switching between soft and hard terrains. As the contact areas of wheels with various terrains and resultant sinkage are dominant factors to ensure fidelity but difficult to determine, a new terrain modeling method for calculating contact area and wheel sinkage is developed using digital elevation map with physical properties. A simulation system is developed, integrating all the above models, and verified with physical experiments and commercial software. The relative simulation errors that have been achieved are less than 5.9% for bogie angles, 6.1% for drawbar pull, and 3.4% for slip ratios, demonstrating high fidelity simulation results.

The increasing application requirements of multilegged walking robots in outdoor environments pose new challenges regarding the design of their teleoperation systems. Some of these challenges arise from the multiple degrees of freedom of the telerobotic system and nonpassive exogenous disturbance. Herein, a novel control system based on a dual-master/single-slave bilateral haptic teleoperation framework using a semiautonomous strategy for hexapod robots walking on deformable rough terrains is proposed. In this teleoperation system, the body velocities and postures of the hexapod robot are determined according to the positions of two haptic master robots. The proposed teleoperator includes a time-domain passivity control approach to compensate for the system’s potential nonpassivity induced by the contact slippage between the foot and the ground. Furthermore, a posture-level bilateral controller is designed to overcome the unpredictable posture vibration. Information about the velocity loss and posture error is displayed to the human operator in the form of haptic force. In the underlying controller of the slave robot, a foot-force optimization algorithm is developed to improve the local autonomy of the teleoperation system. Furthermore, the stability of the system is demonstrated by its passivity. Experimental results indicate that the proposed controllers can provide a stable and transparent bilateral haptic teleoperation system for a hexapod robot under environmental perturbations.

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM’s null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach’s feasibility and effectiveness.

Human-robot collaboration (HRC) significantly extends robotic systems’ applications when working in spaces like houses, hospitals, or laboratories. However, new challenges appear during a close collaboration between humans and robots and imitating the movement of humans by robots. Learning from demonstration (LfD), or kinesthetic teaching, is a popular approach to help teach a robot human behavior by demonstrations without the need to explicitly reprogram the robot for different procedures. In this paper, we propose a method for object manipulation, including lifting, carrying, and lowering the object through a collaboration of a human with a wheeled mobile manipulator (WMM). The WMM is first trained with the help of a human demonstrator to collaborate with the user to execute the task. Then, the WMM will independently cooperate with the user by reproducing the learned skills to perform the same task. The redundancy of the WMM will also be employed to enhance its force exertion capability in the vertical direction to offset the object’s weight. The advantages and effectiveness of the proposed method are investigated through experiments.

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.

As a versatile template characterizing the center of mass movements in legged locomotion, the sagittal spring-loaded inverted pendulum (SLIP) model has been extensively explored in both biomechanics and robotics. Despite concise in mathematical formulation, the accurate analytical representation of the SLIP model is unaccessible due to its intrinsic nonlinearity. This article extends the traditional SLIP model from sagittal hopping into spatially running. A novel perturbation-based approach is proposed to obtain an analytical approximate solution for the 3-D-SLIP model, resulting in a straight-forward closed-form formulation wherein the numerical integration or iteration is avoided. The derived solution does not rely on the negligible gravity assumption as conventional simplification reported in the existing literature and offers satisfactory prediction performance in a wide range of model parameter combinations. The merits of the acquired approximate solution in both critical state of the apex return map and trajectory prediction with high accuracy have been demonstrated via performance evaluation, endowing this approximation the potential in motion planning and gait control for legged robots.

An adaptive neural network (NN)-based finite-time tracking control method is presented for the nonstrict nonaffined nonlinear multi-input-multi-output systems. The hardship of this article is that each subsystem responses to all input variables and any other subsystems of the whole system. Moreover, the uncertainty of the input transition matrix further soars the difficulty of controller design. In this article, NNs are used to approximate these functions with uncertainty automatically. Based on the Lyapunov stability theory, the controller we designed has proven to be semiglobal finite-time stable, implying that all the tracking errors converge to a small neighborhood of the original states in finite time, and the closed-loop system is semiglobal practical finite-time stable. At last, a simulation example is applied to verify the effectiveness of the proposed control algorithm.

This paper addresses a time-optimal point stabilization control for inherent unstable wheeled inverted pendulum (WIP) vehicles using quasi-convex optimization and B-spline adaptive interpolation techniques. First, to handle the difficulty caused by the inherent unstable characteristic, a state feedback control law is introduced to the state-space model deduced with the kinematic coupling relationship between the longitudinal motion and tilt angle of vehicle body. Then, by system discretization, a standard quasi-convex optimization problem is formulated to plan a time-optimal trajectory with various constraints being taken into account, and the optimization problem can be solved by a bisection method combined with solving a series of convex feasibility problems. Next, to obtain the analytic expression of the discrete optimal trajectory, a B-spline interpolation algorithm with adaptive curve refinement is presented based on feature points recognition. Furthermore, as the time-optimal trajectories with analytic expressions, including displacement, velocity, and acceleration trajectories, have been obtained, a proportional-integral-derivative (PID) tracking controller is applied to control the vehicle to track the optimal trajectories and, thus, the time-optimal point stabilization control for the WIP vehicle can be realized. Finally, simulation results of a numerical example is presented to validate the feasibility and effectiveness of the proposed method.

This paper presents a trajectory optimization formulation for planning dynamic trajectories of a six-degree-of-freedom (six-DOF) cable-suspended parallel robot (CSPR) that extend beyond the static workspace. The optimization is guided by low-dimensional dynamic models to overcome the local minima and accelerate the exploration of the narrow feasible state space. The dynamic similarity between the six-DOF CSPR and the three-DOF point-mass CSPR is discussed with the analyses of their feasible force polyhedra. Finally, the transition trajectories of a three-DOF CSPR are used as the initial guess of the translational part of the six-DOF motion. With the proposed approach, highly dynamic motions for a six-DOF CSPR are efficiently generated with multiple oscillations. The feasibility is demonstrated by point-to-point and periodic trajectories in the physics simulation.

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.

The wheel imprint photograph of the rover contains important information such as wheel motion parameters, soil characteristic parameters and movement status of the rover. Segmenting the wheel imprint area from the photograph is an important prerequisite for feature extraction and parameter identification. Traditional image segmentation cannot take both speed and precision into account because they are based on a wide range of image object attributes, such as grayscale threshold, color, texture, gradient, contrast, shape and size. This paper presents an image segmentation method based on the wheel imprint feature. Compared to other common graphics segmentation methods, the morphological characteristics and frequency domain characteristics of the wheel imprint are found out by analyzing the mechanism of the wheel–terrain interaction process, and the eigenvector is also constructed. In the wheel trace feature space, the clustering algorithm is used to divide the image into the trace area and the non-trace area. The segmentation accuracy and processing speed were used to evaluate the algorithm. The experimental results show that the developed algorithm based on the characteristics of wheel imprint formation mechanism is more accurate and efficient than the traditional method.

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%.

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.

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.

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.

Compared with conventional robots, soft structures such as living octopus arms and various soft-robot arms have more degrees of freedom (DOFs) and greater flexibility. Soft robot arms have a considerable range of applications. However, it is arduous to establish a mechanical model for them, because of the hyper-redundant DOFs, and the hyperelasticity and nonlinearity of the soft material. In this study, to investigate the deformation of soft octopus robot arms, the simplified closed-form analytical equations for the curvature and torsion were derived according to the Cosserat theory. A closed-form model for axial constriction was developed. The analytical equations were experimentally validated, and a dynamical simulation of the multi-flexible bodies was performed for a cable-driven soft-robot arm inspired by the octopus. The results show satisfactory accuracy.

The Spring-Loaded Inverted Pendulum (SLIP) has been widely exploited in both biomechanical and robotics research due to its simple form in mathematics and high accuracy in fitting experimental biology data. However the intrinsic nonlinearity of the SLIP dynamics makes accurate analytical representation unavailable. Traditional methods take advantage of numerical integration to handle this issue while several existing analytical approximations focusing on 2D-SLIP model. The 3D-SLIP suitable to physical reality is rarely investigated. This paper presents a novel perturbation-based approach to obtain the closed-form analytical approximations of the 3D-SLIP model in stance phase. In contrast to existing work ignoring the gravitational forces, the proposed approach just relies on assumptions of small leg compression and small leg swept angle. The performance of the derived approximations has been evaluated via comprehensive numerical analysis. The quality of accurate apex prediction promises the approximation as an advantageous and reliable tool for locomotion control of legged robots.

In an autonomous scientific exploration system, the terrain map generated from mapping process integrates sensing information from multiple aspects and lays the base for decision making processes. With the increasing challenges in planetary exploration, equipping planetary rovers with the principles of terramechanics is becoming more and more common, especially on rough or intricate terrain. However, it is difficult for conventional maps with elevation information only to reflect terrain mechanical properties, which play important roles in terramechanics-based simulation or motion control. This study extracts the dominant parameters in terrain bearing and shearing models, and presents a multi-layered grid map with fundamental geometric and mechanical elements. A corresponding mapping scheme based on dense visual input is designed to reconstruct elevation in the map and predict terrain mechanical parameters of the entire visual field. Experiments are conducted to verify the practicability of the approach proposed in a Mars emulation yard with a rover prototype.

Simulation of planetary rovers moving on complex terrains is critical for Mars exploration. Equivalent stiffness is proposed and used to characterize the pressure-sinkage property of terrain, while friction angle to characterize the shearing property. Terramechanics model for calculating forces between rigid wheel and soil is proved to be the same with that contact model for calculating forces between rigid wheel and rock. A Digital Elevation Map with Physical Properties is developed and applied to simulate terrain physical properties along with its geometry information. The established methods are validated using simulation and experimental tests with a three-wheel-rover.

Biologically inspired by the hopping performance of kangaroo, this paper extends the traditional Spring-loaded Inverted Pendulum (SLIP) model by adding an actuation at hip to composite a tail-actuated SLIP (TSLIP) model as an abstract template for gait controller design. To deal with the intrinsic nonlinearity of stance dynamics in the TSLIP model, an analytical approximation is derived by virtue of perturbation technique with gravity correction. Employing the derived solution to construct the apex return map, a gait controller is further devised with a two-layer nonlinear optimization scheme. The outer loop optimizes the tail motion during stance by matching the energy variation between the current and target apex state while the inner loop subsequently selects the optimum touchdown angle by minimizing the difference between the predictive and target apex vector from stride to stride. Additionally, an extended control strategy that embodies a time-dependent pre-positioned policy for swing-leg working in conjunction with the active tail is devised, requiring no priori knowledge of the ground truth to enhance to hopping performance of the TSLIP system. The simulation results have demonstrated the effectiveness of the proposed control strategy for tailed hopping system.

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.

Communication between human operators and robots is important. In this paper, two problems are addressed: (1) how a robot can change its behavior sequences in manipulation tasks with human intervention; (2) how a human operator can transfer information to a robot efficiently. We tackle the first problem by proposing a framework based on finite state machine to model the robot action sequences in manipulation tasks. The framework shows the way in which a robot slides its level of autonomy by considering the result of the transition action and the input of the human operator. The framework can be easily extended to include higher level of autonomy. To tackle the second problem, we proposed a method to encode human intentions into simple mouse gestures. With a discriminative classifier, the robot can recognize the mouse gesture drawn by the human operator and carry out the corresponding action. The classifier is based on PCA and has been tested on our data set collected from four volunteers with an average precision of 96.25%.

The wheels of planetary rovers will slip when they climbs up deformable slopes. On the contrary, the wheels will skid in the longitudinal direction in order to generate resistance force to balance the gravity component when a rover moves down the slopes. The wheel-terrain interaction principles of slip versus skid are quite different, but there is little research about the longitudinal skid mechanics and the relationship of it with the slip mechanics. This paper analyzes the problem of longitudinal slip and skid that occur to a wheel on the slopes with the knowledge of terramechanics. The slip and skid mechanics are compared based on experimental results measured by a single wheel testbed. The piece wise linear function is proposed to predict the drawbar pull and resistance moment under both slip and skid conditions. A semi-empirical equation of predicting the skid mechanics according to the slip mechanics is also provided. The models are verified using the experimental data.

In this paper, an adaptive fuzzy control scheme is proposed for hybrid motion/force of trilateral teleoperation systems with a dual-master-single-slave configuration under stochastic time-varying delays in communication channels. Different from previous works on bilateral teleoperation systems, this paper addresses dual-master trilateral control of a single holonomic-constrained robotic manipulator, where the communication delays are modeled as multiple Markov chains, and the motion/force controls are investigated under consideration of unsymmetric stochastic time-varying delays and system dynamical uncertainties. Using partial feedback linearization, the whole trilateral teleoperation system, which consists of both master and slave manipulator dynamics, is transformed into three subsystems. By integrating Markov jump systems to handle random delays, adaptive fuzzy control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems (FLSs). It is proven that the trilateral teleoperation system is stochastically stable in mean square under specific linear matrix inequality (LMI) conditions, and all the signals of the resulting closed-loop system are uniformly bounded. The proposed scheme is validated by extensive simulations.

Digital watermarking has been used widely for the purposes of copyright protection and copy deterrence for multimedia content. In a forensic watermarking architecture, a buyer-seller watermarking protocol can enable a seller to identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Existing schemes are inefficient in practice for their high bandwidth usage. This paper proposes a buyer-seller watermarking protocol that is efficient from the bandwidth usage point of view. First, multicast that is an efficient transport technology for one-to-many communication is exploited, which can reduce the bandwidth usage significantly. Second, symmetric encryption instead of public-key encryption is performed on the multimedia content, which also can reduce the complexity and communication cost.

Numerical simulation analysis of the motion of wheeled mobile robots is significant for both their R&D and control phases, especially due to the recent increase in the number of planetary exploration missions. Using the position/orientation of the rover body and all the joint angles as generalized coordinates, the Jacobian matrices and recursive dynamic models are derived. Terramechanics models for calculating the forces and moments that act on the wheel-as a result of the deformable soil-are introduced in consideration of the effect of normal force. A rough terrain modeling method is developed for estimating the wheel-soil interaction area, wheel sinkage, and the terminal coordinate. A simulation program that includes the above techniques is developed using Matlab and SpaceDyn Toolbox. Experimental results from a 4-wheeled mobile robot moving on Toyoura soft sand are used to verify the fidelity of the simulation. A simulation example of a robot moving on a random rough terrain is also presented.

Wheeled exploration robots are prone to slip during locomotion on deformable rough planetary terrain, which leads to loss of velocity and extra consumption of energy. Experimental results show that the power required for driving a wheel is an increasing function of its slip ratio; further, the tractive efficiency decreases rapidly after it reaches a peak value when the slip ratio is between 0.05 and 0.2. In this study, wheel-soil interaction terramechanics, which considers the slip ratio as an important state variable, is applied to analyze the quasi-static equations of a planar robot system. The slip ratios of wheels are controllable, but the degree of freedom is the number of wheels minus 1. A generalized algorithm for distributing the slip ratios of all the wheels of a robot to optimize the energy consumption is presented. Experimental and simulation results show that the “equal slip ratio” is at least a sub-optimal solution for optimizing energy consumption. Further, a more robust control method has been developed; this methods aims to equalize the slip ratios of all the wheels while maintaining a constant body velocity on rough terrains, without solving the values of the slip ratios. This method is verified by controlling a virtual four-wheeled robot using dynamics simulations.

The wheel slip ratio is an important state variable in terramechanics research and the control of planetary rovers. Definitions of the slip ratio for a wheel with lugs and methods of estimating it for all wheels onboard have seldom been attempted. This paper presents several definitions for the slip ratio of a lugged wheel, which can be interconverted by altering the shearing radius. Equations for calculating the longitudinal velocity and slip ratio of a wheel moving on rough terrain are deduced from the horizontal speed of the wheel's axle. Wheel-soil interaction experiments were performed for two types of wheels with different radii and lugs of different heights. The drawbar pull, torque, and wheel sinkage were measured using sensors. These data confirmed the effectiveness of the proposed slip ratio definition methods. Furthermore, two slip ratio estimation methods are proposed and verified: a visual information-based method by analyzing the lug traces marked on the terrain with high precision, and a terramechanics-based method in which the equations for the vertical load and torque are solved to estimate the slip ratios of all wheels.

Identifying planetary soil parameters is not only an important scientific goal, but also necessary for exploration rover to optimize its control strategy and realize high-fidelity simulation. An improved wheel-soil interaction mechanics model is introduced, and it is then simplified by linearizing the normal stress and shearing stress to derive closed-form analytical equations. Eight unknown soil parameters are divided into three groups. The highly complicated coupled equations, each of which includes all the unknown soil parameters, are then decoupled. Each decoupled equation contains one or two groups of soil parameters, making it feasible to make a step-by-step identification of all the unknown parameters that characterize the soil. Wheel-soil interaction experiments were performed for six kinds of wheels with different dimensions and wheel lugs on simulated planetary soil. Soil parameters are identified with the measured data to validate the method, which are then used to predict wheel-soil interaction forces and torque, with a less than 10% margin of error. The improved model, decoupled analytical model, and soil-characterizing method can play important roles in the development of both the planetary exploration rovers and the terrestrial vehicles.

To improve the driving smoothness and stability of lunar rover and reduce the vertical dynamic deflection, this paper sets up vibration model for seven degree-of-freedom lunar rover, presents the calculation method for vibration differential equation and frequency response function, and derives the equation for the vertical dynamic deflection of the rover suspension system based on the ascertaining of the input spectral density of road surface. This paper determines the proper range of stiffness of torsion bar spring and damp of the vibration absorber by the calculation of the vertical dynamic deflection of the rover suspension system with Matlab programming