This article focuses on the trajectory tracking problem of mobile manipulators (MMs). Firstly, we construct a position and orientation model predictive tracking control (POMPTC) scheme for mobile manipulators. The proposed POMPTC scheme can simultaneously minimize the tracking error, joint velocity, and joint acceleration. Moreover, it can achieve synchronous control for the position and orientation of the end-effector. Secondly, a finite-time convergent neural dynamics (FTCND) model is constructed to find the optimal solution of the POMPTC scheme. Then, based on the proposed POMPTC scheme, a non-singular fast terminal sliding model (NFTSM) control method is presented, which considers the disturbances caused by the floating-base on the manipulator at the dynamic level. It can achieve finite-time tracking performance and improve the anti-disturbances ability. Finally, simulation and experiments show that the proposed control method has the advantages of strong robustness, fast convergence, and high control accuracy.

To ensure operational accuracy and flexibility in confined environments, the cable-driven hyper-redundant manipulator needs to take into account both compliance and stiffness. Although the cable-driven method enables the manipulator to have adjustable stiffness, the theoretical analyses and studies on the effects of various factors on the stiffness are insufficient, leading to the possibility that the existing variable stiffness strategies may disrupt the equilibrium state of the manipulator. Accordingly, this paper presents the modeling and analysis of joint-to-end variable stiffness for cable-driven hyper-redundant manipulator. First, the multi-layered static models are constructed to decouple and characterise the complex robotic arm system by combining the manipulator kinematics with the virtual work principle. Then, the joint-to-end analytical stiffness models are developed to explore the influencing factors of stiffness, and relevant stiffness indicators are designed to evaluate the stiffness level. Finally, with platform validation and numerical method, the connections between cable tension, joint angle, joint stiffness and end stiffness are analysed, thereby summarising the variable stiffness characteristics of the cable-driven super-redundant manipulator.

This article proposes a practical adaptive fuzzy backstepping control (FBC) scheme for the constrained locus tracking problem of a nonlinear cable-driven manipulator. Specifically, fuzzy logic systems aim to approximate unknown nonlinear terms, and the adaptive backstepping control achieves tracking of the desired motion state by recursively designing the virtual references and control laws. Compared with the existing FBC, the practicality is enhanced in the sense that: 1) simultaneous compliance with prescribed performance and state constraints; 2) no need for the tricky process of finding feasibility conditions for virtual references under strict constraints; and 3) insensitive to system initial conditions for the tracking error convergence of the constrained system. These advanced features lie in the improvement of backstepping protocols through the novel coordinate transformation mechanism, the designed asymmetric barrier Lyapunov functions, and adaptive fixed-time control laws. Application and comparison studies based on a laboratory-developed cable-driven manipulator not only confirm the effectiveness but also show the advantages of the proposed scheme.

This article studies the cooperative output regulation problem for multiple flexible manipulators. First, based on a two-parameter-correlated adaptive law, a new fully distributed observer is proposed. Different from the existing works, the observer does not depend on global graph information and can handle the uncertainties in the leader system. Second, a barrier-function-based sliding mode disturbance observer is proposed for the flexible manipulator to estimate the state-dependent disturbance. This new disturbance observer does not depend on the exact form of the disturbance. We show that the fully unknown state-dependent disturbance can be estimated and rejected in finite time. Finally, based on the proposed sliding mode disturbance observer, a new adaptive controller is proposed. The controller does not require the tip-end state information of the flexible manipulator, which is beneficial for the implementation. Both simulations and experiments show the validity of the presented method.

This article aims to develop a noncascade speed control approach for permanent magnet synchronous motor (PMSM) systems based on the Takagi–Sugeno (T-S) fuzzy model. In noncascade control structures, the lack of firsthand current references imposes a tricky challenge in dealing with current constraints from the circuit and unmatched disturbances caused by load torques. To this end, a virtual reference-based fuzzy noncascade speed control scheme is proposed for PMSM systems. First, unmatched load torques are estimated by a fuzzy disturbance observer. And then, a novel virtual reference planner is constructed to inject the estimated unmatched disturbance into the control channel as feedback compensation. This improves antidisturbance performance, while keeping the current response within the feasible domain and reducing speed overshoot. Furthermore, the noncascade speed regulation problem is converted into a system stabilization problem and addressed by the composite control strategy based on the nonparallel distributed compensation. Finally, closed-loop stability with reduced conservatism is ensured through the fuzzy Lyapunov functions and improved slack variables. Simulations and experiments show that the proposed scheme achieves excellent speed response without overcurrent even under mismatched load torque variations.

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

The multiple automatic guided vehicles (AGVs) pathfinding methods that do not consider the uncertainties of the future motion state may lead to large-scale congestion. To address this problem, this paper proposes an improved conflict-based search (CBS) algorithm with a load-information map. Firstly, the grid map with load information and the evaluation criteria of collision-free pathfinding is constructed. Then, an improved CBS algorithm is designed under a set of constraints on the motion states. This is achieved by performing a search on the constrained conflict tree between individual robots using load-information-map at the high level while exploring efficient single-robot searches with modified penalty function at the low level. Finally, the proposed improved CBS algorithm is tested through simulation and experiment. The results show that it not only finds a collision-free path for multiple AGVs under the motion uncertainties but also obtains a comprehensive optimal solution that considers the evaluation criteria and map load simultaneously.

The tracking path needs to be planned in advance when the mobile robot is in a highly restricted area. In this paper, a modified dynamic window approach (MDWA)-based local path planning method is proposed to guarantee the moving security. First, a strategy for constructing sub-target points online is designed to resolve the non-optimal problem in fixed sub-target point planning by selecting the least cost point on the original path as a sub-target point. Moreover, the path planning evaluation function of the MDWA method are constructed to avoid falling into local minimum or leading to large deviation of the original path distance. Then, end-curve splicing method is used to make the mobile robot return to the original path smoothly. Finally, extensive experiments are performed to verify the effectiveness of the proposed MDWA algorithm.

Obstacle avoidance is essential for mobile robot when applying to dynamic scenarios. Existing methods that based on deep reinforcement learning (DRL) use the position information as the environment states and neural network input to train the robot in a low efficiency manner, because the position information is unable to indicate obstacle’s motion trend. To address this problem, this paper proposes a new motion-prediction-based obstacle avoidance method for mobile robots based on DRL. The position information of dynamic obstacles in time domain is used to construct a motion trend vector, and together with other motion state factors to form the robot motion guidance matrix, which effectively expresses the motion change trend of dynamic obstacles in a period that provides more valuable information for the robot to choose avoidance action. The experimental results show that the safety of avoiding dynamic obstacles is effectively improved.

Pose tracking is one of the most critical techniques in mobile robot navigation, but dynamic environments will greatly reduce its accuracy and robustness. For addressing the above problem, this paper proposes a pose tracking method based on Monte Carlo localization and Entropy-based trimICP (E-trimICP) to achieve accurate and robust robot localization. First, a hybrid noise filtering method that fuses distance filtering and radius filtering is presented to improve the reliability of observation data. Then, an E-trimICP is designed to improve the accuracy of scan matching in dynamic environments. The proposed method can not only enhance the adaptability by determining the trim ratio based on the dynamic degree of the environment, but also improve the operational efficiency through the improved heap sorting. Ultimately, the effectiveness of the proposed pose tracking method is verified by real experiments using a self-developed mobile robot.

In intelligent manufacturing scenarios, efficient path planning methods are of great significance to the safe and stable operation of multiple automatic guided vehicles (AGVs). This paper contributes a path planning method of multi-AGV considering waiting time optimization, which improves the operation efficiency of multi-AGV. Firstly, aiming at the problems of large granularity and low utilization of spatial resources in traditional topology map planning, an augmented topology map is proposed to improve the utilization rate of geospatial information. Based on this map structure, an improved DFS (depth first search) algorithm is presented to obtain multiple alternative paths during a single search. Moreover, based on the selected path, a corresponding model is established to optimize the waiting time of multi-AGV motion planning scheme, which is solved by genetic algorithm. In addition, the simulations on the multi-AGV platform validate the efficiency of the suggested path planning method.

Four-wheeled steerable mobile robots have been widely used in industrial fields. However, robust tracking control is hard to achieve with a fixed maneuver mode. Based on a novel internal coupled sliding mode control (ICSMC) method, this study handles the above challenge by constructing a mode-dependent switched mobile robot (MSMR), and then designing an asynchronous$H_{\infty}$continuous stabilization control scheme. This method governs the system behaviors to perform adaptive-mode allocations asynchronously and smoothly. First, a unified expression of MSMR is formulated, which regards the maneuver modes as optional subsystems. Then, with the coupled sliding surfaces and modified reaching law, an enhanced chattering-free ICSMC technique is established to optimize continuous control inputs for each subsystem. Moreover, using a surveillant criterion that depicts the energy decaying rate, an evaluation rule is built to distinguish the mismatched subsystems and then realize autonomous mode switching for the concerned MSMR. Utilizing piecewise Lyapunov functional technique and mode-dependent average dwell time, new sufficient conditions are derived for global exponential stability and$H_{\infty}$performance. Comparative experiments implemented on the developed MSMR are carried out to verify the effectiveness of the proposed control scheme.

With a modified form of the rapidly- exploring random tree (RRT), RRT* algorithm is an important and effective tool for sampling-based path planning. However, the partial extension and low efficiency of the traditional RRT* make it very difficult to satisfy specific constraints or real-time requirements of mobile robotic scenarios. Based on the double tree structure expansion, a double-tree RRT* (D-RRT*) algorithm is proposed in this paper with the ability to improve space collision detection and search the feasible connection area using constrained nodes. The proposed method can effectively utilize the fast preprocessing ability of a double-tree structure and reduce the implementation of iterations. Meanwhile, node filtering and regression are designed to reduce the node numbers and prevent over space searching. Through the validation examples of mobile robots, it is shown that the proposed D-RRT* method can search for a global safe path with enhanced efficiency and convergence as compared with conventional methods.

With mounted robot arm, the compound trackless automatic guided vehicle (AGV) has strong flexibility and adaptability in industrial environments. However, due to the space constraints of the robot arm, the path planning in complex large scenes is hard to achieve high efficiency, and it is easy for falling into local minimum and stagnation. In this paper, a novel AGV path planning algorithm is proposed on the basis of map preprocessing to improve the planning efficiency and guarantee operating safety. First, by integrating the multiple constraints including the obstacles and robotic position/posture, the preprocessing method of the environmental map is proposed utilizing obstacle expansion and Delaunay triangulation. Then, to achieve better convergence and global optimization capacity, the global path searching is modified by (1) constructing the OpenList with priority queues; (2) exploring the adaptive rules for step size and dynamic weighted heuristic function. Finally, combining linearization and cubic Hermite interpolation method, the planned path is smoothed to enhance the movement stability and energy consumption rate. Simulation analysis and experimental results verify the feasibility, efficiency and superiority of the proposed path planning method.

global localization is essential for pose initialization and pose recovery. However, for the lack of prior information, global localization is always unreliable and time consuming, especially in featureless and dynamic industry environment. To alleviate the negative influence of such environment, this paper uses reflector as landmarks. Then, several maps including labeled occupancy grid map and multi-resolution likelihood field are proposed to model the positions of landmarks as well as ordinary obstacles. Furthermore, a branch and bound method is employed to achieve fast global search based on those proposed maps. Through experiments in a real industry application, the reliability and efficiency of our proposed global localization method is verified.

This paper proposes an efficient and robust localization recovery method considering time variation for Monte Carlo localization (MCL), which can handle the kidnapped robot problem (KRP) with improved recovery speed and success rate. The presence of particles near the real robot’s pose is necessary for the localization recovery of MCL. Therefore, the generation number and position of random particles are the vital factors to solve KRP. It is generally assumed that the robot cannot move instantaneously, so the size of the search space where robot may appear should be time-dependent after the KRP occurs. Given these considerations, a restricted search space is firstly constructed as a set subject to a time-varying normal distribution, which can effectively narrow the search space and estimate the probability that the robot may appear. In addition, a short-long term random particle generation strategy considering time variation is designed to availably determine the number of random particles according to the change of the likelihood and time. And then, the random particles are spread into the restricted search space. Finally, the above particle set is integrated into the MCL for localization recovery. The effectiveness of the proposed method is verified through real scene experiments.

Four-wheel-steering mobile robots (FMR) are extensively-adopted for manufacturing applications. However, the robust trajectory tracking of FMR is hard to achieve due to the complex disturbances. For addressing the challenge, an asymmetric barrier function-based adaptive control (ABFAC) method is presented here for obtaining a robust direct yaw moment control scheme for the FMR. The praiseworthy features of the proposed ABFAC method are twofold: (a) It does not need the information concerning the upper boundary information of the unknown uncertainties; (b) By introducing the barrier function, the output signals are guaranteed to be within the desired neighborhood since the sliding mode gains will grow once the disturbance derivatives increase. In this context, the proposed ABFAC method drives the resultant trajectory tracks the variation tendency of the disturbances, guaranteeing robust closed-loop responses. The global stability of the FMR can be ensured theoretically. Finally, experimental results of real-time FMR are provided to validate the applicability of our ABFAC method.

The equipment used in kinematic calibration schemes is generally expensive and time-consuming, and requires skilled engineers. This paper proposes a new measurement method for identifying kinematic errors on a six degree of freedom industrial robot with a single laser displacement sensor. The three-dimensional deviations of the end-effector are calculated by an algorithm with data collected from the scanning procedure. Then, the kinematic parameters are identified by combining the joint angles, the deviations involved error information and an error model. The accuracy and efficiency are improved by the laser displacement sensor and the simple scanning procedure. The method presented in this paper can also be expanded to other serial industrial robots. The experimental results validate the effectiveness of the proposed method.