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[Display omitted] • We implement more accurate optical flow tracking by the assistance of IMU. We use the rotated tracked points obtained by an IMU to initialize the estimation of the optical flow, and this can improve the reliability of the initial feature points. To obtain accurate feature matching, a stereo baseline constraint and ring matching are used to remove outlier points. The experiments show that the information fused with a stereo camera and an IMU achieves real-time optical flow tracking and more accurate feature matching than the LK optical flow method. Compared with the feature matching method in ORB-SLAM3, the proposed method is more lightweight and faster. • We develop a high-precision SLAM framework that integrates stereo vision and an IMU to tackle the problem of pose inaccuracy that results from the fast movement of a stereo camera and insufficient view overlap between frames. We build a new objective function based on VINS to reduce the complexity of the computation. We adopt a sliding window to ensure the real time performance of the system, and the marginalized information is added as a prior in the objective function. The experimental results demonstrate that the localization accuracy of the proposed SLAM framework is better than that of OKVIS, VINS-Mono and VINS-Fusion. • We propose a fast method for constructing dense point maps to estimate depth values based on stereo vision and an IMU. The depth value computed by SGBM (semi-global block matching) is regarded as the initial value to update the depth of the deep filter, which can improve the convergence rate. We further adopt the TSDF (truncated signed distance function) to fuse the depth images obtained by stereo matching and build a dense map. The experiments show that the proposed stereo dense reconstruction method can obtain a deeper image, less convergence time for the estimated picture and fewer updating frames than the REMODE (probabilistic, monocular dense reconstruction) method. Mobile vehicles require accurate localization and dense mapping for motion planning. In this paper, we propose a dense map construction algorithm based on a light-and-fast stereo visual-inertial navigation system (VINS). A tightly coupled nonlinear optimization method is used to calculate the position of adjacent keyframes. An optical flow tracking method fused with IMU information and ring matching constraints is used to improve the matching accuracy and speed of the feature points. In addition, we obtain the pose and depth values using the semi-global block matching (SGBM) method, which are used as the initial values of the depth filter to update the depth image and improve the convergence speed. Then, we further use the Truncated Signed Distance Function (TSDF) method to construct the dense map. We compare our algorithm with state-of-the-art algorithms on the EuRoc dataset and then compare the estimated depth image using the proposed algorithm and the point cloud construction with the probabilistic monocular dense reconstruction (REMODE). The experiments show that the proposed algorithm can obtain more accurate localization than VINS and OKVIS, as well as a faster tracking speed, a better depth map, a lower convergence time for the estimated image and a lower number of updated frames than REMODE. [ABSTRACT FROM AUTHOR]

Intelligent mobile vehicles asks for the ability to learn to comprehend immediate surroundings similar to human cognition and autonomously navigate in an unknown scene. Although simultaneous localization and mapping (SLAM) can construct a geometrically surrounding map, the map contains little semantic information. This paper presents a novel stereo visual-inertial system with loop closure detection based on a semantic-topological map framework. In our system, a hybrid 3D point cloud semantic-topological mapping framework is used to realize autonomous navigation simply by providing a map for path planning and meanwhile for storing semantic information under a dynamic environment. A stereo visual-inertial system can obtain more accurate visual-inertial odometry than a monocular system, and a visual-inertial system is capable of real-time map correction when the localization module detects a loop closure. This paper adopts state-of-the-art Mask R-CNN components to obtain 2D semantic information and project it to a semi-dense 3D semantic-topological map built by stereo visual-inertial SLAM. We perform experimental evaluation of our proposed system on the EuRoC MAV datasets and we also compare it with other competitive methods. Our results demonstrate that the proposed method is more effective than the other competitive methods. [ABSTRACT FROM AUTHOR]

The simultaneous localization and mapping (SLAM) method based on the brain-space cognitive model can improve the localization accuracy of the robot through memory. The traditional method has poor robustness in complex environments such as intense motion, weak texture, and illumination changes, and lack of understanding of the environment. Our work is based on RatSLAM. To improve the robustness, we propose a multi-sensor fusion algorithm, which integrates a camera with an IMU. To improve the understanding of the environment, we use the Yolo to extract the semantic information of objects and store it in the topological nodes and construct a 2D topology map. Yolo-v4 is used to replace Yolo-v3 to improve the accuracy of semantic tag extraction. We validated our approach in the pubic dataset and real environments. The mean RMSE APE is reduced by 51.716% compared to RatSLAM. Also, our method can build a semantic topological map of the environment. • A multi-sensor fusion algorithm based on the RatSLAM algorithm to improve the localization accuracy. • The mean RMSE APE was reduced by 51.716% on the EuRoC dataset compared to RatSLAM. • A novel semantic topology RatSLAM framework is developed that introduces Yolo-v3 and Yolo-v4 to RatSLAM. • The robot still finds the object even if its location changes because of location information automatically preserved in the semantic topology node. [ABSTRACT FROM AUTHOR]

Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic obstacle avoidance method based on the Variable-dimensional Flower Pollination (VFP) algorithm, which can avoid dynamic obstacles under an unknown environment. The grid map is used to describe the environment and the shearing map refreshment strategy is used to improve refreshment efficiency. The fitness function is designed by combining Chebyshev distance with Euclidean distance, which can reasonably evaluate the planned path. Simulation results with traditional Flower Pollination algorithm and VFP algorithm are compared. The simulation results show that the VFP algorithm has a faster learning speed than the traditional Flower Pollination algorithm. To verify the simulation result, the VFP algorithm is implemented on the NAO robot, and the experiment result demonstrates that the Variable-dimensional Flower Pollination algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

The paper proposes a novel method for the nonlinear redundantly actuated parallel robot based on force/position hybrid control structure. In order to solve the limitation of making a compromise for internal model controller, a two-DOF fractional order internal model control algorithm combining the internal model control principle and the fractional order theory is proposed for the position branch of the parallel robot redundantly actuated. This algorithm can realise the adjustment of the dynamic performance and anti-interference of 6PUS-UPU respectively. Aiming at the big force control error fractional order internal model, fuzzy control theory and the fractional order internal model controller are integrated into a new controller-fuzzy fractional order internal model(FFOIM) force control algorithm. Then Admas/Matlab simulation results demonstrate that the proposed algorithm can further reduce the driving force error of the system, and also retain the strong anti-interference of fractional order internal model controller. [ABSTRACT FROM AUTHOR]

The 6PUS-UPU parallel robot is a kind of multi-input multi-output, coupled and highly nonlinear system. Conventional control methods are no longer effective for this complex 6PUS-UPU parallel robot. In this paper, the Inner Model Control (IMC) method in the position control loop and the Fractional Order Fuzzy Proportional–Integral Derivative (FOPID) control method in the force loop are investigated. To study the validness of the IMC method and the FOPID control method, their performance is compared with other published control methods under disturbance and noise, such as three-loop control method, PI control method and Fuzzy Proportional–Integral-Derivative control method. The simulation results clearly demonstrate that the robustness of IMC method and the FOPID control method outperform other methods. [ABSTRACT FROM AUTHOR]

Redundant actuated parallel robot is a multi-input and multi-output (MIMO) system which usually works in an uncertain environment. In this paper, the force/position hybrid control structure of 6PUS-UPU redundant actuation parallel robot is designed, and proportional–integral (PI) and model predictive control (MPC) cascade control strategies are used in the redundant branch of 6PUS-UPU redundant actuation parallel robot. The MPC algorithm is used in the current loop of the permanent magnet synchronous motor (PMSM) to restrain the motor parameter uncertainty and external disturbances influence on motor control. The MATLAB/ADAMS joint simulation method based on virtual 6PUS-UPU redundant actuation parallel robot prototype is used to test the performance of the proposed control strategy. The performance of proposed PI-MPC control strategy is compared with the traditional PI–PI control strategy. The simulation results show that the MPC controller can improve the tracking ability of the motor torque, guarantee the system robustness under the parameter variations and load disturbance environment. [ABSTRACT FROM AUTHOR]

Recently, wireless communication has faced with “green” challenge. The emergence of the “green wireless communication” means environment protection and energy saving. A “green wireless communication” is aimed at energy conservation and emissions reduction on the basis of reducing communication radiation and ensuring the quality of communication in the wireless communication network. According to related statistics, the energy consumption of the base station accounts for more than 70% in wireless communication network. So, it is important to reduce the energy consumption of the base station to realize the energy saving of mobile communication system. As a result, the base station sleeping strategy in coordinated multipoint (CoMP) communication is a promising method to solve this problem. In the base station sleeping strategy, base station with the most light traffic is off, and the users in this cell are served by the surrounding base stations with CoMP communications. However, since the downlink performance is also important for users, we should save the energy as well as keeping a perfect downlink performance. This paper presents a control theory to study the base station sleeping strategy optimization issues with CoMP communications. Specifically, to make decisions for the base station sleeping strategy, we apply the threshold method and reinforcement learning (RL) algorithm. We develop the multi-step Q-learning of the RL algorithm to optimize the base station sleeping strategy. Simulation results are provided to show the process and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The two important problems of SLAM and Path planning are often addressed independently. However, both are essential to achieve successfully autonomous navigation. In this paper, we aim to integrate the two attributes for application on a humanoid robot. The SLAM problem is solved with the EKF-SLAM algorithm whereas the path planning problem is tackled via Q -learning. The proposed algorithm is implemented on a NAO equipped with a laser head. In order to differentiate different landmarks at one observation, we applied clustering algorithm on laser sensor data. A Fractional Order PI controller (FOPI) is also designed to minimize the motion deviation inherent in during NAO’s walking behavior. The algorithm is tested in an indoor environment to assess its performance. We suggest that the new design can be reliably used for autonomous walking in an unknown environment. [ABSTRACT FROM AUTHOR]

This paper addresses a virtual force field between mobile robots and obstacles to keep them away with a desired distance. An online learning method of hybrid force/position control is proposed for obstacle avoidance in a robot environment. An Elman neural network is proposed to compensate the effect of uncertainties between the dynamic robot model and the obstacles. Moreover, this paper uses an Elman fuzzy adaptive controller to adjust the exact distance between the robot and the obstacles. The effectiveness of the proposed method is demonstrated by simulation examples. [ABSTRACT FROM PUBLISHER]

This paper addresses the T-S fuzzy robust dynamic output-feedback control problem for a class of nonlinear continuous-time systems with parametric uncertainties and premise variables. First, based on the control input matrix and output matrix, the parametric uncertainties are assumed to be a subsystem, which is described as a linear fractional. Secondly, the nonlinear continuous-time systems are described by the Takagi–Sugeno (T–S) fuzzy model. Then the new dynamic output feedback controller is designed based on the T–S fuzzy model and the linear fractional (parametric uncertainties), and the sufficient conditions for robust stabilization are given in the form of linear matrix inequalities (LMIs). Compared with previous work, the developed methods not only have abilities to handle the fuzzy system with premise variables but also can deal with the parametric uncertainties effectively. The results are further extended to a mobile robot case and a chemical process case. Finally, numerical examples are performed to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively. [ABSTRACT FROM AUTHOR]

The H∞ stability analysis and interval type‐2 Takagi‐Sugeno (T‐S) fuzzy control is studied for a class of interval type‐2 T‐S fuzzy systems. Firstly, the interval type‐2 T‐S fuzzy model is employed to approximate the system plant. Secondly, the delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed. Thirdly, two classes of stability conditions in terms of linear matrix inequalities (LMIs) are derived, and the closed‐loop system is asymptotically stable and prescribed H∞ performance is achieved. The delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed to relax the control design conditions. The descriptor redundancy method is employed for avoiding the crossing vectors between the system gain matrices and controller gain matrices. The delay‐dependent Lyapunov functional with descriptor redundancy matrix is introduced, and the upper and lower membership functions information is considered. Some slack matrices are introduced via the upper and lower membership functions information to solve the obtained LMIs. Finally, simulation examples are performed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The H-infinity stability analysis and delay-dependent Takagi–Sugeno (T–S) fuzzy dynamic output feedback control are proposed for the T–S fuzzy discrete networked control systems with time-varying communication delay and multipath packet dropouts. T–S fuzzy model is employed to approximate the discrete networked control system with time-varying state delay and external disturbance. Stochastic system theory and Bernoulli probability distribution are employed to describe the time-varying communication delay and multipath packet dropouts. Delay-dependent T–S fuzzy dynamic output feedback controller is designed. The delay-dependent T–S fuzzy dynamic output feedback controller is employed to relax the design conditions and enhance the design flexibility. The delay-dependent Lyapunov–Krasovskii functional, stochastic system theory and Bernoulli probability distribution are introduced to guarantee the stochastic mean-square stability and prescribed H-infinity performance. Some slack matrices are introduced to reduce the computation complexity. Finally, simulation examples are presented to show the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper addresses the stability analysis and dynamic output feedback control for a class of T–S fuzzy networked control systems with mixed time-varying delays and interval distributed time-varying delays. Firstly, the T–S fuzzy model is employed to approximate the networked control system based on the system model. Secondly, the dynamic output feedback controller with hybrid intermittent feedback control law is designed for the T–S fuzzy networked control system. Thirdly, the delay-dependent stability conditions are derived by introducing the Lyapunov functional with Leibniz–Newton formula. Compared with previous works, both the mixed time-varying delays and interval distributed time-varying delays are considered in the T–S fuzzy networked control system. The control design conditions are relaxed because of the proposed controller. By introducing the Lyapunov functional with Leibniz–Newton formula, the linear matrix inequalities are solved effectively by the standard convex optimization algorithm. Finally, the simulation examples and some computing results are given to show the effectiveness and advantage of the proposed method, respectively. [ABSTRACT FROM AUTHOR]

Summary: This research addresses the stability analysis and adaptive state‐feedback control for a class of nonlinear discrete‐time systems with multiple interval time‐varying delays and symmetry dead zone. The multiple interval time‐varying delays and symmetry dead zone are considered in the nonlinear discrete‐time system. The multiple interval time‐varying delays are bounded by the nonlinear function with unknown coefficients, and the symmetry dead zone is considered without the knowledge of the dead zone parameters. The adaptive state‐feedback controller is designed for the nonlinear discrete‐time systems with multiple interval time‐varying delays and dead zone. The discrete Lyapunov‐Krasovskii functional is introduced, such that the solutions of the closed‐loop error system converge to an adjustable bounded region and the state errors can be rendered arbitrarily small by adjusting the adaptive parameters. The designed adaptive state‐feedback controller does not require the knowledge of maximum and minimum values for the characteristic slopes of the dead zone. Finally, three simulation examples are given to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the stability analysis and Takagi–Sugeno (T–S) fuzzy dynamic output feedback control for a class of T–S fuzzy systems with multiple time-varying delays and unmatched disturbances. Firstly, the T–S fuzzy model is employed to represent the system based on the system input matrix and output matrix. Secondly, the T–S fuzzy dynamic output feedback controller is designed based on the T–S fuzzy model. Thirdly, the line-integral fuzzy Lyapunov functional is employed and the boundary information of membership functions is considered in the stability analysis. Compared with previous works, the multiple time-varying delays and unmatched disturbances are considered in the T–S fuzzy system. The T–S fuzzy dynamic output feedback controller and T–S fuzzy system employ the same number of fuzzy rules but different membership functions to relax the control design conditions. The line-integral fuzzy Lyapunov functional is employed and the boundary information of membership functions is considered to obtain the less conservative stability conditions. Finally, two simulation examples are performed and some comparison figures/results are presented to show the effectiveness and advantage of proposed method, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the stability analysis and adaptive state feedback control for a class of nonlinear discrete-time systems with multiple interval time-varying delays and non-symmetric dead-zone input. The multiple interval time-varying delays uncertainties are bounded by the nonlinear function with unknown coefficients. The non-symmetric dead-zone input without the knowledge of the dead-zone parameters is considered for the nonlinear system. The smooth adaptive state feedback controller is designed. By introducing the new Lyapunov-Krasovskii functional, it can be seen that the solutions of the closed-loop error system converge to an adjustable bounded region. In general, the proposed adaptive state feedback control strategy does not require the knowledge of maximum and minimum values for the characteristic slopes, and the knowledge of all system states. The control design conditions are relaxed because the system output is available for the controller design. Finally, three simulation examples are performed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• Congruence transformation method and Schur complement are employed to determine the controller gain matrices. • (2) Cone complementarity linearization algorithm is constructed to convert controller design problem into minimization constraints. • (3) Stochastic Bernoulli theory is employed to describe characteristics of random occurrence for packet dropouts in signal transmission network. The stochastic Takagi-Sugeno (T-S) fuzzy discrete dynamic output feedback control is investigated for a class of stochastic T-S fuzzy discrete networked control systems. Firstly, the T-S fuzzy model and stochastic Bernoulli theory are employed to approximate the discrete system plant. Secondly, the T-S fuzzy model and stochastic Bernoulli theory are employed to design the stochastic T-S fuzzy discrete dynamic output feedback controller. Thirdly, the stochastic Bernoulli theory is employed to describe the characteristics of random occurrence for multipath packet dropouts. The delay-dependent discrete Lyapunov functional and Schur complement are employed, the closed-loop system is stochastically finite-time bounded stable subject to (c 1 , c 2 , ω ¯ , N , R). The Schur complement and congruence transformation matrix are introduced to determine controller gain matrices. The cone complementarity linearization is applied to convert controller design into nonlinear minimization constraints for solving the nonconvex problem caused by fuzzy-basis-dependent LMIs. Simulations are given to show the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

• Proposed method is applied in tunnel diode circuit system to show application. • Schur complement and congruence transformation method are employed. • Cone complementarity linearization algorithm is designed to solve nonconvex problem. • LMIs stability conditions are derived via fuzzy-basis-dependent Lyapunov function. • Computation complexity of solving LMIs is reduced and nonconvex problem is solved. In this article, the problem of exponential mean-square stability analysis is discussed for uncertain networked control systems expressed by a stochastic T-S fuzzy model. In general, the characteristics of random occurrence for multipath packet dropouts often exist in the signal transmission network. For dealing with this difficult point, a dynamic output feedback strategy combining stochastic Bernoulli theory is employed. Then, delay-dependent stability conditions are derived and closed-loop system is exponentially mean-square stable by designing fuzzy-basis-dependent Lyapunov functional. Furthermore, in terms of linear matrix inequalities (LMIs) technology, sufficient conditions are gained to guarantee the prescribed H-infinity performance. Different from previous literatures, the congruence transformation method is employed to determine controller gain matrices for reducing the computation complexity of solving LMIs. Finally, the proposed method is applied in tunnel diode circuit model to verify the applicability. [ABSTRACT FROM AUTHOR]

Taking into account the effect of structural compliance, inverse dynamics of the active over-constrained parallel manipulator 6PUS–UPU with five degrees of freedom is solved in this article. Firstly, the relationship between driving forces and actuated force screws of each limb is derived. Then the coordination of elastic deformation between limbs which consider the effect of gravity and inertia is acquired. Finally the unique solution of driving forces for the active over-constrained parallel manipulator is derived by incorporating the force equilibrium equation of the moving platform. To validate the theoretical derivation, dynamics simulation model of manipulator based on rigid–flexible mixed structure is shown and numerical examples are given. Comparison with the traditional method of dynamics based on pseudo-inverse is also made. Finally, a feasible experimental method, as an effective test to the theoretical calculation, is proposed and applied on the prototype. [ABSTRACT FROM AUTHOR]

This paper addresses the H-infinity Takagi-Sugeno (T-S) fuzzy control for a class of T-S fuzzy discrete networked control systems with random interval communication delays and random sector nonlinearities. Firstly, the T-S fuzzy model is employed to approximate the discrete networked control system and the ℓth-order Rice fading channels model is introduced in the system model. Secondly, the T-S fuzzy dynamic output feedback controller with ℓth-order Rice fading channels output is designed for the T-S fuzzy discrete networked control system. Thirdly, the discrete delay-dependent Lyapunov-Krasovskii functional, stochastic system theory and Bernoulli probability distribution are employed to derive the stability conditions in terms of linear matrix inequalities (LMIs). Compared with previous works, the fading channels in the signal transmission are described clearly by setting the different channels coefficients of the ℓth-order Rice fading channels model. The closed-loop system is exponentially mean-square stable and prescribed H-infinity performance is guaranteed by designing the T-S fuzzy dynamic output feedback controller. The factorizations in the polynomial and the congruence transformation matrices are introduced to solve the LMIs, such that the controller gain matrices are determined. Finally, simulation examples are presented to show the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the new stability criteria for the asymptotic stability of time-delay systems via integral inequalities and Jensen inequalities. Firstly, not only the known constant time delay, but also the unknown time-varying delay is considered for the linear system. Secondly, the new delay-dependent Lyapunov-Krasovskii functional based on the double integral inequalities and Jensen inequalities is introduced, such that the linear system with time-delay is asymptotically stable. Thirdly, two classes of delay-dependent stability conditions in terms of linear matrix inequalities (LMIs) are derived, such that the control design conditions are relaxed and computation complexity is reduced. Compared with previous works, the larger feasible solution region and less conservative results are obtained. Finally, some numerical examples are performed to show the effectiveness and advantage of the proposed method. [ABSTRACT FROM AUTHOR]