Search

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.

This article studies synchronization control of a novel devised treadmill-based testing apparatus for wheeled planetary rover roaming at low speed. To offer satisfactory tracking performance and to ameliorate internal conflicts amongst individual treadmills during rover-treadmill interaction, a decentralized synchronization control strategy integrating an adaptive friction compensation scheme is proposed. To overcome the nonlinear friction effect at low-velocity scenarios, the LuGre model based friction compensation scheme is constructed with a dual-observer structure that can handle parametric uncertainties without recourse to the high-resolution encoder-required parameter identification. With the proposed control design, asymptotic stability of the closed-loop system is guaranteed with the tracking error and synchronization error converging to zero in presence of parametric uncertainties. Experimental results demonstrate the effectiveness of the proposed control strategy, which significantly improves the tracking performance in comparison with the existing proportional differential (PD)-type controller and synchronization controller without friction compensation.

Drawbar pull and driving torque are usually applied to characterize the mobility and energy consumption, respectively, of wheeled planetary rovers (WPRs) traversing sandy terrain. Owing to the complexity of the grouser-terrain interaction, neither can be modeled as a closed-form analytical expression of the terrain mechanical parameters. Complexity limits their online applications. Moreover, it is difficult to estimate the terrain mechanical parameters. To solve these issues, based on equivalent wheel sinkage, both the drawbar pull and driving torque can be modeled as a linear function of physically measurable quantities in the absence of terrain mechanical parameters, which is demonstrated using single-wheel experiments conducted with four types of wheels and two sand types. The influence of a sophisticated grouser-terrain interaction on wheel sinkage can be explicitly determined using the linear driving torque model. Furthermore, the validated linear drawbar pull equation is used to estimate the slope-climbing capability of a four-wheeled planetary rover prototype by computing the slope-climbing coefficient, and the relative error can be limited to 10%. In addition, the wheel driving torque of the rover prototype can also be accurately predicted.

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.

The slippage appears on soft terrains for wheeled mobile robots (WMR), and generates a motion deviation at the contact area, which can induce poor command-tracking performance (meaning difference between WMR's actual motion and operator's command) and instability for its teleoperation. Our previous studies have presented some approaches to conservatively ensure the system stability, and a new slippage-dependent teleoperator is proposed herein to further improve the teleoperation performance. The kinematic model is augmented in the presence of slippage, and slippage-induced unstable elements are revealed by passivity analysis. To compensate for the activity and improve the command-tracking performance induced by slippage, the combination of a slippage-dependent forward feedback controller and a traditional position-error-based controller is proposed. Experiments validate that the proposed method can yield a stable system with good command-tracking performance and force transparency compared with previous studies.

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.

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.

Unmanned surface vehicles (USVs) are vessels that operate without any crew onboard. There is an increased demand for USVs in recent years, particularly for the use of water quality monitoring and ocean data mapping. In China, USVs are widely used as a luring fish boat which acts as the assisting boat of light luring seine vessel. One of the main problems of such boat is that the traditional propulsion system is poorly matched with the high energy consumption that is required during certain specific operation, which results in poor vessel performance. A hybrid electric propulsion system configuration solution is proposed to increase the overall propulsion efficiency of such USVs. The typical operating profile was identified and a comprehensive simulation was conducted to demonstrate the compatibility during vessel operations. An intelligent equipment selection analysis was also carried out to recommend the optimal equipment selection by considering a multiobjective problem. The result shows that the configuration solution proposed can reduce fuel consumption and the optimal intelligent selection method can provide a suitable selection solution for decision makers. This article highlights an energy management strategy focusing on the threshold method based on support vector machine pattern recognition. A multiobjective particle swarm optimization algorithm based on the dynamic inertia weight and chaotic motion was used to optimize the equipment selection by considering fuel consumption and emissions. The proposed propulsion system configuration and equipment selection solution can be implemented for the design of USVs, which has a routine fixed operating pattern.

Innovation training in engineering is an important facet of technical education in universities across China. Robotics training is a new course that has been developed using an informatization-based engineering training system at the Engineering Innovation Practice Center of the Harbin Institute of Technology. In this study, we propose a pedagogical method that combines virtual and on-site modes of teaching. Feedback obtained from students enrolled in the course showed a positive response to it. They claimed that they gained valuable knowledge from it that enabled them to carry out the relevant tasks in a better manner and more quickly. The feasibility of the proposed scheme was verified by a virtual simulation.

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.

With the increasing applications of wheeled mobile manipulators (WMMs), new challenges have arisen in terms of executing high-force tasks while maintaining precise trajectory tracking. A WMM, which consists of a manipulator mounted on a mobile base, is often a kinematically redundant robot. The existing WMM configuration optimization methods for redundant WMMs are conducted in the null-space of the entire system. Such methods do not consider the differences between the mobile base and the manipulator, such as their different kinematics, dynamics, or operating conditions. This may inevitably reduce the force exertion capability and degrade the tracking precision of the WMM. To enhance the force exertion capability of a WMM, this letter maximizes the directional manipulability (DM) of the manipulator, with consideration of the joint torque differences, first in Cartesian space and then in the null-space of the robotic system. To maintain precise end-effector trajectory tracking, this letter proposes a novel coordination method between the mobile base and the manipulator via a weighting matrix. The advantages and effectiveness of the proposed approach are demonstrated through experiments.

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.

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.

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.

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%.

As a well-explored template that captures the essential dynamical behaviors of legged locomotion on sagittal plane, the spring-loaded inverted pendulum (SLIP) model has been extensively employed in both biomechanical study and robotics research. Aiming at fully leveraging the merits of the SLIP model to generate the adaptive trajectories of the center of mass (CoM) with maneuverability, this study presents a novel two-layered sagittal SLIP-anchored (SSA) task space control for a monopode robot to deal with terrain irregularity. This work begins with an analytical investigation of sagittal SLIP dynamics by deriving an approximate solution with satisfactory apex prediction accuracy, and a two-layered SSA task space controller is subsequently developed for the monopode robot. The higher layer employs an analytical approximate representation of the sagittal SLIP model to form a deadbeat controller, which generates an adaptive reference trajectory for the CoM. The lower layer enforces the monopode robot to reproduce a generated CoM movement by using a task space controller to transfer the reference CoM commands into joint torques of the multi-degree of freedom monopode robot. Consequently, an adaptive hopping behavior is exhibited by the robot when traversing irregular terrain. Simulation results have demonstrated the effectiveness of the proposed method.

Legged robots have demonstrated significant achievements in recent years. Some legged robots have considerable flexibility and movement abilities. However, certain obstacles restrict the practical application of legged robots, such as their high energy consumption. The energy consumption of a legged robot is significantly higher than that of a wheel robot of the equivalent size for the same walking distance. Reducing the energy consumption of legged robots is important for their practical application and further development. This study proposes a quadratic-programming force-distribution controller, which minimizes the energy consumption of hexapod robots. The controller reduces the energy consumption by optimizing the instantaneous power of the robot at each time step. In a simulation environment, the proposed method reduced the energy consumption by up to 9.43% and 6.30% in flat terrain and sloped terrain, respectively, compared with two other methods. In hexapod robot experiment, the proposed method can reduce energy consumption by 5.72% compared with position control.

The tractive capability of Multi-wheeled mobile robots (WMRs) depends heavily on the performance of the driving control of their steerable wheels. The recently proposed concept of a pseudo-driven wheel (PDW) provides a simple, versatile, and effective way to improve the tractive capability. However, its focus is to eliminate the internal forces between the wheels and save energy; therefore, it cannot provide sufficient driving force to the body on rough terrains. This paper proposes a control strategy to adjust the power output of a PDW along the directions of two degrees of freedom, an approach that improves the trajectory tracking control of WMRs, especially on rough terrains. To achieve the desired active following control of the PDW, an active disturbance rejection controller is used to compensate the disturbances arising from the wheel-terrain interaction. The Adams and Simulink are used for a joint simulation of which results show that the proposed control strategy is necessary for accurate trajectory tracking, for which the feasibility and effectiveness were verified by the experimental results. In summary, the trajectory tracking accuracy of the WMR and its tractive capabilities on rough, sandy terrain were improved.

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.

A recursive dynamic modeling and control is presented for the dual-arm manipulator with elastic joints carrying a common object. For decoupling the effect of elastic joints, the dynamic modeling approach is based on the classic recursive Newton-Euler method but involves high-order derivatives of motion and force variables. The high-order inverse kinematics which is needed in the motion control is presented firstly. With the classic Recursive Newton-Euler (RNEA) method, two-order dynamic model of the dual-arm robot is established, for decoupling the effect of the elastic joints, the form of four-order dynamic model is presented, meanwhile, combining with the high-order dynamic model of the carried object, the completed Dual-Arm Elastic Joints Newton-Euler Algorithm (DA-EJNEA) is established. Then the feedback linearization method is adopted to the motion control based on the DA-EJNEA. Finally, to verify the effectiveness of the proposed method, feedback linearization method based on the DA-EJNEA and the computed torque method based on the dynamic model with rigid joints are used to control the dual-arm coordinated system respectively, the simulation results illustrate the feedback linearization method based on the DA-EJNEA has an obvious advantage for trajectory tracking of the object in the operation space, it behaves reasonable potentials for the model-based control.

An enhanced failure mode effect analysis (FMEA) based risk assessment for subsea compressor systems was proposed in this study. The enhanced model was established using a combination of fuzzy analytic hierarchy process (FAHP), fuzzy comprehensive evaluation, and FMEA. Different from the traditional FMEA model, the model improved the capability to identify system faults and to further measure the risk of a subsea compressor system through effective qualitative and quantitative analyses. A case study was then conducted to demonstrate the capability of the developed method in a complete risk assessment for a subsea compressor system and the subsystems and subcomponents with low reliabilities were identified. Comparing the enhanced model with previous models, it is found that the potential risks of some components are changed, and the components with higher potential risks in the system are identified. Sensitivity analysis to investigate the impact of parameters of subcomponents on system reliability was also performed.

The hexapod robots equipped with six legs have higher stability and adaptability to challenging terrains than other legged robots with fewer legs. The ability of hexapods to traverse challenging terrains largely depends on practical planning approaches on their footstep sequence. However, suppose the stability of the robotic system is insufficiently considered with the footstep planning method, it cannot track the planning results in some extremely complex terrains, e.g., foot slippage or robot overturn. In this work, we develop a quasi-static equilibrium footstep planning method for hexapod robots to traverse challenging terrains. The core of this planning method is the proposed 3D quasi-static equilibrium support region (3D QESR), which can be employed as a constraint for the planning method to ensure the quasi-static stability of the hexapod robots. A new graph search algorithm for footstep sequence planning is also presented. The simulation and experiment results show that the proposed 3D QESR method has superior performance in bypassing unstable irregular regions compared with the widely used support polygon method.

Purpose The purpose of this paper is to enable autonomous door-opening with unknown geometrical constraints. Door-opening is a common action needed for mobile manipulators to perform rescue operation. However, it remains difficult for them to handle it in real rescue environments. The major difficulties of rescue manipulation involve contradiction between unknown geometrical constraints and limited sensors because of extreme physical constraints. Design/methodology/approach A method for estimating the unknown door geometrical parameters using coordinate transformation of the end-effector with visual teleoperation assists is proposed. A trajectory planning algorithm is developed using geometrical parameters from the proposed method. Findings The relevant experiments are also conducted using a manipulator suited to extreme physical constraints to open a real door with a locked latch and unknown geometrical parameters, which demonstrates the validity and efficiency of the proposed approach. Originality/value This is a novel method for estimating the unknown door geometrical parameters with coordinate transformation of the end-effector through visual teleoperation assists.

This letter proposes a synthetic aperture radar (SAR) target recognition method via joint classification of deep features fused by multi-canonical correlation analysis (MCCA). A convolutional neural network (CNN) is designed for feature learning from original SAR images. For the multiple feature maps from different convolution layers, they are fused based on the MCCA to maintain the relevance while eliminating the redundancy. Afterwards, the joint sparse representation (JSR) is employed to jointly represent the fused deep feature vectors from different convolution layers under the constraint of their inner correlations. Based on the reconstruction errors from JSR, the target label can be classified. The proposed method can make full use of the multi-level deep features by using the correlations among the same layer and between different layers. Experiments are investigated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set and the results confirm the performance of th...

The scale parameter is one of the key factors which influence the structure and locomotion characteristics of the legged robot. The scale effect mechanism of animals in nature was revealed, and the influence of scale parameters on animal was analyzed. This paper establishes a dynamic model of insect-imitating hexapod robot. A high-fidelity simulation platform for hexapod robot was established based on Vortex, and the foot-ground interaction mechanics model was established and applied to the developed simulation platform. Based on the existing six-legged robot prototype, the validity of the relevant model and the fidelity of the simulation platform are verified. The influence of a robot’s mass and characteristic size on its feature locomotion was analyzed. The foot force rises with the increase of the whole robot mass, and the foot force of the unit robot mass decreases with the increase of the whole robot mass, eventually tending to a fixed value. The maximum joint torque rises with the increase of the whole robot mass. The system power rises with the increase of the whole robot mass, but the system power of unit robot mass is basically a constant value. The peak system power decreases with the increase of the distance between the front and the rear leg, and the joint torque rises with the increase of the distance between the front and rear leg. The related research results have guiding significance and reference value for the system design of hexapod robots.

Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-and-white image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the gray-scale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.

The articulated rotating speed is one of the important parameters to determine the drive devices and actuating devices of joints for legged robots. Compared with the small-scale multi-legged robots, the range of the output speed of the joint should be as accurate as possible for the large-load-ratio multi-legged robots. To reasonably select the devices of joints, the maximum walking speed method is proposed to quickly and accurately obtain the range of articulated rotating speed by taking an electrically driven large-load-ratio six-legged robot as an example. To prove the rapidity, accuracy, and conciseness of the maximum walking speed method, the analyses of the forward kinematics and inverse kinematics of the robot are implemented based on the Denavit-Hartenberg method. However, only one range of articulated rotating speed is effectively confirmed in a single leg. Through rotating one of the joints to achieve the maximum speed index of the robot, the maximum walking speed method is employed to establish the mathematical relationships between the articulated rotating angles and the maximum walking speed index of the robot. The ranges of the output speeds of all joints are accurately obtained. The simulation verification and walking experiments of the prototype are, respectively, carried out. The results of the simulation and walking experiments show that the maximum walking speed method is reasonable and effective in calculating the range of articulated rotating speed. The proposed method in this paper can be reliably applied to the development of large-load-ratio multi-legged robots.

Door-opening is a critical problem for robots used for rescue purposes in nuclear power plants (NPPs). A force and torque (F/T) sensor is not used in the rescue task for door-opening in the NPPs environment. It is, therefore, necessary to study the dynamic model of door-opening. The NPPs door and a door handle dynamic model are significant to the mechanical design, simulation, and dynamic feed-forward control of an NPP robot. To obtain the accurate force data of door-opening, a method based on a gravity compensation algorithm combined with a transformation of the contact force of the end effector is proposed. This paper analyzes the structural features and dynamics model of a fire door and a door handle. Methods for identifying the model parameters are also developed by combining a Nelder-Mead simplex search with the least-squares algorithm. The extensive door-opening experiments were carried out in this paper, and the results validate the fidelity of the derived dynamic models of the door and the door handle.

The foot-terrain interaction mechanics of human is important in understanding the biped locomotion mechanism. This paper aims to investigate the effect of walking speed and the hardness of the terrain on foot-terrain interaction mechanics. In the experiments, mobile portable plantar mechanical measuring insoles with 107 sensor elements were used to gauge the plantar force and contact area in real-time. Seven subjects with healthy feet have participated in typical experiments. The statistical methods including correlation analysis, univariance analysis, and systematic identification are primarily used to obtain several main results. Bimodal functions in describing the total pressure-time relations were performed at slow and regular speeds but only one hump is left if walking at fast paces; they can be unified and summed by three subsections' unimodal functions. The forefoot produces a large peak impact force in slow and regular quasi-static walking on both hard ground and deformable terrains, while the heel absorbs more dynamic impact shocks in fast walking on hard ground. The torques generated by the ankle joints are calculated based on plantar force or its derived information such as zero moment point. Some implications are drawn, for example, the dynamically changed positions of zero moment point (ZMP) for humans are similar to that generated by reptiles wiggling through the flowable terrains; increasing the stride length on the hard ground produces more impact vibrations than quickening paces but more effective in accelerating walks on deformable terrains.

This paper discusses a problem that has plagued researchers for a long time regarding the dynamic modeling of a multiple degree-of-freedom (multi-DOF) manipulator such that its manipulation exhibits a higher computational efficiency and accuracy. Unknown friction, unknown gravitational torque, an uncertain moment of inertia, and severe joint coupling are the primary disturbing factors in multi-DOF manipulator modeling. In addition, joint flexible problems caused by the integration of harmonic drives increase the modeling complexity. Hitherto, no effective method has been found to address these problems. The virtual decomposition (VD)-based method exhibits the advantages of joint dynamics decoupling and minor computation compared with the traditional Lagrangian formulation or Newton-Euler formulation. In this study, an estimation method for the deformation-related torque of harmonic drives is established based on a novel experimental model; subsequently, this method is utilized in the VD-based model for the multi-DOF manipulator. Hence, the decoupling dynamic model for the manipulator considering joint flexibility is established. The performance of this new method has been evaluated by a contrast simulation with the Newton-Euler formulation, and the multi-DOF manipulator control simulation and experiment have been conducted with a VD-based model as a feedforward compensator to verify its performance in real-time control. The results demonstrated the validity and efficiency of e proposed approach.

In order to comprehensively evaluate the landing performance of vertical-landing vehicle and increase the probability of successful landing, a sagittal dynamics model of legged-type vertical landing vehicle on landing process was established, including the elastic-plastic collision model of multi-rigid body system to predict discontinuous process. The displacement-velocity diagram is used to investigate the influence mechanism of the initial tilt angle and the initial height on the trajectory of the center of mass of the vertical-landing vehicle in various typical landing modes. In addition, the leg buffering behavior of the vehicle is investigated. The buffering mechanism of touched-down legs of different time periods under different initial conditions were accordingly explored and analyzed. The results showed that the buffers in legs that touch the ground first are more sensitive to changes of the falling height, while the initial tilt angle has more influence on the buffers of legs that subsequently touch the ground more. Unlike that the influence of the initial tilt angle on the attitude recovery of the vehicle will continue throughout the landing process, and gradually decease as the convergence, the effect of the initial falling height mainly focused on the initial and final stages of the landing process. The phase diagram is a favorable angle for investigating and analyzing the effect of initial conditions on the entire landing process of the vertical landing vehicle.

Modular actuation units’ development dramatically expands the compatibility and expandability of the modular active vibration isolation system (MAVIS) in space missions. However, due to the current shortness of a unified framework, manipulating such a redundantly actuated multi-input-multi-output (MIMO) nonlinear system assembled by modular units often involves painstaking challenges and repetitive trial. We proposed a unified coordinated manipulation and multi-objective vibration control framework with the ultimate goal of effectively vibration isolation for such systems. This framework, built on a general model and inherently coupling analysis, incorporates optimized manipulation with inversion system improved multi-objective control. Compared with the traditional multivariable feedback control, this framework effectively reduces the controller’s order while ensuring the system’s multiple objectives and frequency constraints. Experiments and simulations demonstrate the effectiveness of the proposed framework in achieving coordinated manipulation and vibration attenuation on a physical system and indicate the potential role of its application for a family of modular active vibration isolation systems.

With the widespread applications of wheeled mobile robots (WMR) in various fields, new challenges have arisen in terms of designing its control system. A special challenge is the soft terrain which can induce both of the longitudinal slipping and the lateral sliding. This paper proposes a new method for haptic teleoperation control of a WMR with lateral sliding. In this teleoperation system, the linear velocity and the angular velocity of the WMR separately follow the master haptic interface’s positions. Due to the functional benefits of displaying the lateral sliding information as haptic feedback to the operator, the lateral velocity is segregated and only perceived by the human operator. Then the teleoperator for the master robot and the slave WMR is designed based on the passivity theory. Experiments of the proposed controller demonstrate that this approach results in stable bilateral teleoperation with a satisfactory tracking performance.

It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.

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.

In some hazardous or inaccessible applications, such as earthquake rescue, as a substitute for mankind, robots are expected to perform missions reliably. Unfortunately, the failure of components is difficult to avoid due to the complexity of robot composition and the interference of the environment. Thus, improving the reliability of robots is a crucial problem. The hexapod robot has redundant degrees of freedom due to its multiple joints, making it possible to tolerate the failure of one leg. In this paper, the Fault-Tolerant Tripod (F-TT) gait dealing with the failure of one leg is researched. The Denavit&ndash, Hartenberg (D-H) method is exploited to establish a kinematic model for the hexapod robot, the Jacobian matrix is analyzed, and it is proved that the body can be controlled when three legs are supported. Then, an F-TT gait phase sequence planning method based on a stability margin is established, and a method to improve stability is proposed. The trajectory for the center of gravity (COG) and foot is studied. Finally, a simulation model and prototype robot experiments are developed, and the effectiveness of the proposed method is verified.

This paper proposes a dynamic point-to-point trajectory planning technique for three degrees-of-freedom (DOFs) cable-suspended parallel robots. The proposed technique is capable of generating feasible multiple-swing trajectories that reach points beyond the footprint of the robot. Tree search algorithms are used to automatically determine a sequence of intermediate points to enhance the versatility of the planning technique. To increase the efficiency of the tree search, a one-swing motion primitive and a steering motion primitive are designed based on the dynamic model of the robot. Closed-form expressions for the motion primitives are given, and a corresponding rapid feasibility check process is proposed. An energy-based metric is used to estimate the distance in the Cartesian space between two points of a dynamic point-to-point task, and this system’s specific distance metric speeds up the coverage. The proposed technique is evaluated using a series of Monte Carlo runs, and comparative statistics results are given. Several example trajectories are presented to illustrate the approach. The results are compared with those obtained with the existing state-of-the-art methods, and the proposed technique is shown to be more general compared to previous analytical planning techniques while generating smoother trajectories than traditional rapidly exploring randomized tree (RRT) methods.

Capacitance aging of DC link capacitors in voltage source inverters (VSIs) is a common fault which can lead to instability of the DC voltage. In such a failure state, although the VSI can still work, its performance gradually deteriorates, resulting in a shorter service life of the equipment. Here, an online monitoring and fault diagnosis method for capacitance aging based on evidence reasoning (ER) rule is presented. Features from the DC link voltage data with different levels of capacitance aging are extracted, and data features are generated as pieces of diagnostic evidence, which are then combined according to the ER rule. Finally, capacitance aging fault levels were estimated using the combined results. This method has better diagnostic performance compared to a backpropagation (BP) neural network approach and can be used to flexibly define the relative weighting of each evidence parameter depending on the application. This approach can therefore be widely used for fault diagnosis of an array of different devices.

With the application of new energy ships equipped with large-capacity batteries/ultracapacitors in oceans, inland rivers and lakes, the need for high-power wireless charging systems has become increasingly urgent. Based on the analysis of the characteristics of ship charging operation, this paper selected the structure of loosely coupled transformer and introduces its core technology. Then the basic principle of the wireless charging system for the ship is introduced, and the scheme of 1.2 MWwireless charging system is designed according to the specific application.2.5

Teleoperation control of the multiple degrees-of-freedom (DOF) robot requires a strategy for improving its controllability and adaptability. A novel bilateral haptic teleoperation approach (multi-master single-slave) is proposed for a hexapod robot in which the first master control the primary task of the slave, e.g. the linear/steering motion; meanwhile, the second master can perform a minor task, e.g. to interact with the environment by leg. The designed teleoperation system consists of two parts: body-level and leg-level. In body-level subsystem, the linear/angular velocity of the slave body follow the first master‘s position, and the velocity loss caused by the system disturbances is fed back to the operator in the form of haptic force. In leg-level subsystem, a modified four-channel (4CH) teleoperation control architecture is proposed for the manipulable leg, which can be regarded as a manipulator, so as to guarantee the performance of the position/force tracking in the subsystem subject to parametric uncertainties, environmental perturbation and unmeasurable interaction force. Additionally, the stability of the multi-DOF bilateral haptic teleoperation system are verified via absolute stability criterion and Lyapunov theorem, respectively. Experiments of the proposed approach demonstrate that it can result in stable and transparent bilateral teleoperation with haptic force feedback.

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.

To control autonomous walking of a legged robot, it is essential to obtain the instantaneous velocity and posture of the robot. This paper presents a full body state estimation algorithm for a hexapod robot to estimate the velocity and posture of the trunk body without geometric knowledge of the environment. The velocity and posture estimates of the hexapod robot with passive compliant ankles are further processed by a data fusion method that is proposed based on the extended Kalman filter (EKF) technique, utilizing the leg kinematics model of the robot and the readouts from an on–board inertial measurement unit (IMU). The absolute footholds of the hexapod robot are estimated together with the velocity and posture of the trunk body, with consideration of intermittent ground contacts. Experiments have been conducted on both flat and uneven terrains, and the results have confirmed the effectiveness of the proposed approach.

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.

Motion planning plays an important role in the performance optimization of legged robots. This paper presents a method to minimize the impact force and energy consumption effectively by providing an integrated strategy of motion planning subject to velocity and acceleration constraints. The parameters defined for the motion planning are computed to generate the foot trajectory. A foot–terrain interaction model and an energy-consumption model are formulated to evaluate the contact force and power consumption for statically stable gaits. The proposed method has been implemented on a hexapod robot. The acceleration of foot landing is reduced, and constant velocity control of the trunk body with passive compliant ankles is achieved for reducing the impact force and energy consumption. Extensive experiments have been carried out, and the experimental results have demonstrated the effectiveness of the proposed method in comparison with a conventional method.

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.

Cable-driven parallel robots (CDPRs) are gaining increasing attention due to their low weight, cost, and power consumption characteristics. Adaptive CDPRs are special cable-driven systems in which the locations of the pulley blocks can be modified. In this paper, we propose a new type of adaptive CDPR that can passively modify the attachment points on the end-effector (moving platform) without augmented kinematic redundancy, but this depends on the cable wrapping over these adaptive attachment points. We demonstrate the proposed approach on some practical cases of planar adaptive CDPRs to expand the workspace and dexterity of the robot especially for the large rotation capability. The proposed robot can be passively reconfigured to several configurations. The kinematics modeling and static equilibrium of the adaptive CDPRs are established. Scale optimization of the adaptive attachment point is performed. The results show that this design can effectively increase the workspace, especially the rotation workspace, without increasing the actuation redundancy and kinematic redundancy.

Wheel sinkage detection is of tremendous significance for planetary rover mobility optimization control and prevention of serious wheel sinking. Wheel sinkage is regarded as the distance between the lowest point of the wheel in the soil and the horizontal flat terrain on relative research. For rovers moving on rough terrains, the above definition is not reasonable because the horizontal flat terrain cannot be found. New wheel sinkage definition and detection method are proposed, based on vision. The sinkage definition rationality is analyzed for various terrain conditions. The discrete mathematical wheel sinkage calculation model, for which the input is acquired through a visual method, is built. Saturation of wheel–soil interaction image is adjusted by a dynamic piecewise nonlinear adjustment method The image is processed into a binary image based on HSI (Hue, Saturation, Intensity) color space. The wheel–soil boundary is extracted from the corrected wheel region outline according to its morphological features. The wheel sinkage and equivalent terrain interface angles are calculated through the discrete mathematical wheel sinkage calculation model. Sinkage definition rationality and detection method applicability are experimentally validated in the four typical terrain conditions (flat, bulgy, sunken, and uneven terrains). Particularly, the experimental results prove the sinkage detection method has high accuracy for flat terrain, for which, the deviation of the sinkage modulus is less than 2% of the wheel radius. The sinkage detection method is proved to have fairly reasonable adaptability to complex illumination conditions, based on the experimental results for different illumination conditions.

With the widespread use of multi-legged robots in various applications, new challenges have arisen in terms of designing their control systems, one of which is posed by the multiple degrees of freedom of the robotic legs. This paper proposes a novel method for the bilateral teleoperation control of a hexapod robot by using a semi-autonomous strategy. In this teleoperation system, the body velocities of the slave robot and the displacements of the master robot are mapped to each other. The angular velocities of the joints of the legs rely on independent planning to achieve a horizontal movement. A controller is designed based on the difference between the expected velocity and the actual velocity of the body, and the difference is fed back to the operator in the form of haptic force. Therefore, the transparency of the control system is guaranteed by increasing the damping compensation both in the master and slave robots. In addition, the stability of the bilateral teleoperation control system of the hexapod robot is guaranteed by passivity theory, and the proposed method is verified by conducting semi-physical simulation experiments.

Training a stroke gesture classifier by using the state-of-the-art Convolutional Neural Network method requires a large sample size to achieve good performance. This becomes a serious problem when users want to add new gestures to the system because adding so many samples is time-consuming and expensive. In this paper, we propose an iterative synthetic data generation method to solve this problem. The method takes in one user-input template gesture which is modeled by Bezier curve and can generate thousands of samples for training. We propose two different modeling approaches so the method can be applied to both mono and multi-stroke gestures. By applying perturbation to the control points, we can obtain enough samples for training. The generation process is carried out in an iterative way, so the variability in different categories of stroke gestures can be balanced. The variability is measured by the dynamic time wrapping method. The proposed method is tested on our own dataset and two published datasets. Our method outperforms methods with fixed generation process and reaches high recognition accuracy.

Purpose The purpose of this paper is to extend the usage of stroke gestures in manipulation tasks to make the interaction between human and robot more efficient. Design/methodology/approach In this paper, a set of stroke gestures is designed for typical manipulation tasks. A gesture recognition and parameter extraction system is proposed to exploit the information in stroke gestures drawn by the users. Findings The results show that the designed gesture recognition subsystem can reach a recognition accuracy of 99.00 per cent. The parameter extraction subsystem can successfully extract parameters needed for typical manipulation tasks with a success rate about 86.30 per cent. The system shows an acceptable performance in the experiments. Practical implications Using stroke gesture in manipulation tasks can make the transmission of human intentions to the robots more efficient. The proposed gesture recognition subsystem is based on convolutional neural network which is robust to different input. The parameter extraction subsystem can extract the spatial information encoded in stroke gestures. Originality/value The author designs stroke gestures for manipulation tasks which is an extension of the usage of stroke gestures. The proposed gesture recognition and parameter extraction system can make use of stroke gestures to get the type of the task and important parameters for the task simultaneously.