Kinematic Control Research Articles

Optimized trajectory tracking control with disturbance resistance for wheeled mobile robot

When unavoidable disturbances exist in practical applications, the unicycle wheeled mobile robot (WMR) with non-holonomic constraint is challenging to be accurately controlled in finite time. An optimized dual closed-loop strategy is proposed in this paper to solve the trajectory tracking control problem of the unicycle WMR. The control strategy with fast time convergence and anti-disturbance properties consists of kinematic and dynamics controllers and an optimization algorithm. The kinematic controller is designed with an improved backstepping method to eliminate the non-holonomic constraints. The robust sliding mode control (SMC) method with fixed-time convergence is used to construct the dynamics controller. The optimization algorithm enhances the performance of the strategy and achieves accurate control. The global convergence and robustness of the strategy are theoretically demonstrated. The effectiveness of the strategy is verified by comparison in simulations.

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Design of a robust intelligent controller based neural network for trajectory tracking of high-speed wheeled robots

The control design of wheeled mobile robots is often accomplished based on the robot’s kinematics which imposes critical challenges in the motion tracking control of such systems. In the related literature, most works designed dynamic controllers based on the terms of voltage or torque; however, the velocity is often utilized in industrial and commercial applications. To address this issue, this paper focuses on the design of a velocity-based control for the trajectory-tracking problem of mobile robots in practical applications including measurement acquisition. The control design of the mobile robot is realized in two separate parts using kinematic control and dynamic control. The design of kinematic control is carried out based on the robot’s kinematics where the motion is described without considering the forces and torques. A radial basis function (RBF) neural network (NN) based on the model-free controller is designed for the dynamic controller of mobile robots without relying on the system dynamics. In the proposed dynamic control, a time-delay estimation is adopted to estimate the disturbances and noises in the mobile robot and remove them from the feedback loop control. Several typical scenarios of mobile robots with high-speed movements are conducted to assess the feasibility of the proposed NN controller-based time-delay estimation under noises and disturbances. To show the supremacy of the suggested controller, the dynamic responses of the mobile robot test system are compared with the prevalent controllers. The extensive simulation and real-time examinations reveal the capability of the proposed NN controller-based time-delay estimation to control high-speed mobile robots under high-level noises and disturbances.

A Two-Stage Facial Kinematic Control Strategy for Humanoid Robots Based on Keyframe Detection and Keypoint Cubic Spline Interpolation

A plentiful number of facial expressions is the basis of natural human–robot interaction for high-fidelity humanoid robots. The facial expression imitation of humanoid robots involves the transmission of human facial expression data to servos situated within the robot’s head. These data drive the servos to manipulate the skin, thereby enabling the robot to exhibit various facial expressions. However, since the mechanical transmission rate cannot keep up with the data processing rate, humanoid robots often suffer from jitters in the imitation. We conducted a thorough analysis of the transmitted facial expression sequence data and discovered that they are extremely redundant. Therefore, we designed a two-stage strategy for humanoid robots based on facial keyframe detection and facial keypoint detection to achieve more natural and smooth expression imitation. We first built a facial keyframe detection model based on ResNet-50, combined with optical flow estimation, which can identify key expression frames in the sequence. Then, a facial keypoint detection model is used on the keyframes to obtain the facial keypoint coordinates. Based on the coordinates, the cubic spline interpolation method is used to obtain the motion trajectory parameters of the servos, thus realizing the robust control of the humanoid robot’s facial expression. Experiments show that, unlike before where the robot’s imitation would stutter at frame rates above 25 fps, our strategy allows the robot to maintain good facial expression imitation similarity (cosine similarity of 0.7226), even at higher frame rates.

Leader-follower control and APF for Multi-USV coordination and obstacle avoidance

To overcome the limitations of a single USV in task execution, this paper investigates the issues of cooperative motion and obstacle avoidance for USV swarm. The leader-follower control approach is chosen as the strategy for coordinating the formation of multiple USVs. Kinematic and kinetic controllers are designed using sliding mode control, with a saturation function replacing the sign function to avoid chattering. The validity of the kinematic and kinetic controllers is demonstrated using the Lyapunov stability theorem. To tackle the problem of goal reachability when obstacles are nearby in the artificial potential field method for obstacle avoidance, the repulsive potential field function is modified by including the distance factor between the USV and the target point. To address the problem of local minimum, the simulated annealing algorithm is incorporated into the traditional artificial potential field method. Multi-condition simulation experiments are conducted using MATLAB. The experimental results demonstrate that multiple USVs can form a formation in a short time and maintain the formation while navigating, exhibiting high robustness. Furthermore, they are able to effectively navigate through complex marine environments, successfully avoiding obstacles and ultimately reaching their target destination.

Hybrid trajectory tracking control of wheeled mobile robots using predictive kinematic control and dynamic robust control

AbstractTrajectory tracking control of wheeled mobile robots (WMRs) is still a remarkable problem for many applications. In the present paper, a hybrid control is presented based on dynamic and kinematic equations of motion for wheeled mobile robots in the presence of the sum of the external disturbances and parametric uncertainty. The designed control for the WMR utilizes control and guidance to reach the reference path. In many studies, a control strategy is normally employed for WMR. However, in this study, hybrid control was used for the mentioned purpose. Akin to other studies, the kinematic control scheme here was based on the predictive control, and the dynamic control scheme was designed based on the robust control. Therefore, in this article, having introduced the kinematic model, a nonlinear predictive control was proved and designed. In the next step, a finite‐time integral type terminal sliding mode control (FITSMC) was designed based on the nonlinear dynamic model in order to automatically adjust the control gain and eliminate online disturbances and destructive chattering phenomena completely. In particular, a finite‐time disturbance observer was designed to estimate the external disturbances. The proof of the new proposed control scheme was presented using Lyapunov stability theory and numerical results. The mentioned integrated scheme, including predictive control (outer loop) and nonlinear adaptive control (inner loop), ensures the convergence and optimal tracking performance of all signals, as a result of which the tracking errors can arbitrarily converge to the origin in a finite time. In the final step, the simulation results were presented to show the effectiveness of the proposed scheme using MATLAB software, and the introduced control design was compared with a similar controller quantitatively and qualitatively.

Kinematic modeling and control of orthogonal omnidirectional wheeled two-dimensional conveyor platform

This paper proposes an orthogonal omnidirectional wheeled two-dimensional conveyor platform and the inverse kinematic equations based on the cell model and the omnidirectional wheel model are derived, respectively. The two models use the way of the circumcircle of the cargo box and the dot product method to obtain the cell or omnidirectional wheel directly contacted under the cargo box, respectively. This paper uses YOLOV8_OBB and Strong SORT target tracking algorithms to assign tracking IDs to the cargo box and output the box position information. A feedback mechanism for faulty omnidirectional wheels or faulty cells is proposed, where the faulty area is classified as impassable during path planning and the A* path planning algorithm is used for path planning to avoid the faulty area. A visual feedback-based path tracking is adopted to enable timely correction when the cargo box deviates from the established path. The experimental results show that the omnidirectional wheel-based model improves the path-tracking accuracy by 44.52% and reduces the average power consumption by 2.6 times compared to the cell-based model.

A feedforward kinematic error controller with an angular positioning deviations model for backlash compensation of industrial robots

The accuracy of industrial robots, typically on the order of several millimeters, has inhibited their adoption in advanced aerospace manufacturing applications, such as robotic machining. For this reason, extensive investigations have been conducted to develop solutions to improve their accuracy. These solutions can be categorized into offline and online compensation strategies, both of which have advantages and limitations. Offline compensation has been shown to improve the accuracy of industrial robots by two to three orders of magnitude; however, the sensitivity of these solutions to environmental changes and external disturbances can degrade their performance. In contrast, online compensation, which utilizes real-time measurement and compensation, is robust to these changes; however, the performance of these solutions is limited by the causality of time, preventing them from compensating fast changing errors such as backlash. In this work, a novel kinematic modeling strategy, which models the robot’s angular positioning deviations to predict when backlash will occur, is integrated into a novel online kinematic error compensation framework to leverage the advantages of both solutions. By integrating the proposed kinematic model into the online compensation framework, backlash errors can be predicted and preemptively compensated to improve performance. The performance of the combined system was evaluated using ball bar measurements, where it was shown to improve the circularity of the ball bar motion by 25 %.

Design of motion control system for unmanned wheeled electric vehicle

Abstract The unmanned wheeled electric vehicle is a weapon platform that draws significant attention in current and future battlefields, and research on its motion control is fundamental. This paper introduces the design of a 6×6 unmanned wheeled electric vehicle and discusses the motion control methods applicable to this vehicle. The electric drive system of this vehicle comprises six independent asynchronous motors and corresponding motor controllers. A comprehensive controller designed based on DSP connects the six motor controllers. As it is an experimental platform, the motion control algorithms are deployed on an onboard computer, which connects to the comprehensive controller. Using the Backstepping method, the kinematic control algorithm for the 6×6 unmanned wheeled electric vehicle is designed. The global asymptotic stability of the control system is proven through the Lyapunov function, and its control performance is verified via simulation experiments. Finally, the system is deployed and tested on the actual vehicle. Experimental results show that the motion control system designed in this paper enables the unmanned wheeled electric vehicle to achieve effective motion control. The control laws derived from the Backstepping method are relatively simple and have low requirements for the sampling period. In practical systems, output speed magnitude and rate of change must be limited to stay within allowable system parameters.

Adaptive Imune Fuzzy Quasi-Sliding Mode Formation Tracking Control for Wheeled Mobile Robots with Obstacle Avoidance Under Incidence of Uncertainties and Disturbances: An Artificial Immune Systems Inspired Approach

This paper presents an adaptive immune fuzzy quasi-sliding mode kinematic control integrated with a PD dynamic control for the trajectory tracking and the leader-follower formation control by nonholonomic differential-drive wheeled mobile robots under incidence of uncertainties and disturbances. An immune regulation mechanism bio-inspired approach with reaction effect established by novel fuzzy rules set to adjust the control effort adaptively is designed, also using a fuzzy boundary layer and introducing an adaptation law for the immune portion gain online adjustment in such a way that they can also avert parameter drift, dealing with the drawbacks of a classic first-order sliding mode control, suppressing chattering and still maintaining the robustness with no a priori knowledge of the bounds of the disturbances. An obstacle avoidance strategy with a reactive method and variable avoidance radius is also proposed. The stability analysis is performed based on the Lyapunov theory. Simulation results demonstrate the proposed control effectiveness.

Knee joint position sense and kinematic control in relation to motor competency in 13 to 14-year-old adolescents

BackgroundMotor competence (MC) is a key component reflecting one’s ability to execute motor tasks and is an important predictor of physical fitness. For adolescents, understanding the factors affecting MC is pertinent to their development of more sophisticated sporting skills. Previous studies considered the influence of poor proprioceptive ability on MC, however, the relationship between lower limb joint position sense, kinematic control, and MC is not well understood. Therefore, the aim of this study was to determine the relation between joint position sense and kinematic control with MC in adolescents during a lower limb movement reproduction task.MethodsThis study was a cross-sectional design. Young people (n = 427, 196 girls and 231 boys) aged 13 to 14 years were recruited. A movement reproduction task was used to assess joint position sense and kinematic control, while the Movement Assessment Battery for Children (mABC-2) was used to assess MC. In this study, participants were categorized into the Typically Developed (TD, n = 231) and Probable Developmental Coordination Disorder (DCD, n = 80) groups for further analysis of joint position sense, kinematic control, and MC between groups.ResultsKinematic data, specifically normalized jerk, showed a significant correlation with MC. There was no correlation between knee joint position sense and MC, and no group differences between DCD and TD were found.ConclusionsJoint position sense should not be used as a measure to distinguish TD and DCD. Rather than joint position sense, control of kinematic movement has a greater influence on the coordination of the lower limbs in adolescents. Movement control training should be implemented in the clinical setting to target kinematic control, rather than focus on joint position sense practice, to improve motor competency.Trial Registration IdentifierNCT03150784. Registered 12 May 2017, https://clinicaltrials.gov/study/NCT03150784.

Kinematic and neuromuscular characterization of cognitive involvement in gait control in healthy young adults.

The signature of cognitive involvement in gait control has rarely been studied using both kinematic and neuromuscular features. The present study aimed to address this gap. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudorandom oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). Subjective mental workload (raw NASA-TLX), cognitive performance (mean reaction time and d-prime), kinematic (steadiness, variability and complexity in the mediolateral and anteroposterior directions) and neuromuscular (duration and variability of motor primitives) control of gait were assessed. The cognitive performance and the number and composition of motor modules were unaffected by simultaneous walking, regardless of the optic flow condition. Kinematic and neuromuscular variability was greater under POF compared to NOF conditions. Young adults sought to counteract POF by rapidly correcting task-relevant gait fluctuations. The depletion of cognitive resources through dual-tasking led to reduced kinematic and neuromuscular variability and this occurred to the same extent regardless of simultaneous working memory (WM) load. Increasing WM load led to a prioritization of gait control in the mediolateral direction over the anteroposterior direction. The impact of POF on kinematic variability (step velocity) was reduced when a cognitive task was performed simultaneously, but this phenomenon was no modulated by WM load. Collectively, these results shed important light on how young adults adjust the processes involved in goal-directed locomotion when exposed to varying levels of task and environmental constraints.

Kinematic control in a four‐wheeled Mecanum mobile robot for trajectory tracking

AbstractThe challenges of the modern world require mobile robots with the ability to navigate in congested environments with high levels of manoeuvrability. Therefore, the Mecanum wheel may be viable for addressing this challenge. This paper presents the experimental results of a kinematic control strategy, which involves considering the dynamic model as a black box, with only the input and output signals being known. To do this, high‐level and low‐level controllers are formulated and explained. The high level aims to control the desired position of the mobile robot, which can be useful for navigation tasks. On the other hand, low‐level control involves nested controllers to regulate the speed of the mobile robot wheels. Both levels are related and computed through pure kinematics transformations with a dSPACE ds1103 card and MATLAB/Simulink software. In total, 120 experiments were conducted to determine the repeatability of the tests, using the combination of three widely explored control techniques in the literature: proportional‐integral‐derivative (PID), PID plus sliding modes, and PID plus quasi‐sliding modes. The experiments conducted are described in detail, and the results are analysed using statistical indices based on the RMS error and percentage improvement.

Kinematic Modeling and Control for an Elephant-Trunk Soft Manipulator Considering Hysteresis

Kinematic Modeling and Control for an Elephant-Trunk Soft Manipulator Considering Hysteresis

A Dini-Derivative-Aided Zeroing Neural Network for Time-Variant Quadratic Programming Involving Multi-Type Constraints With Robotic Applications.

Time-variant quadratic programming (QP) with multi-type constraints including equality, inequality, and bound constraints is ubiquitous in practice. In the literature, there exist a few zeroing neural networks (ZNNs) that are applicable to time-variant QPs with multi-type constraints. These ZNN solvers involve continuous and differentiable elements for handling inequality and/or bound constraints, and they possess their own drawbacks such as the failure in solving problems, the approximated optimal solutions, and the boring and sometimes difficult process of tuning parameters. Differing from the existing ZNN solvers, this article aims to propose a novel ZNN solver for time-variant QPs with multi-type constraints based on a continuous but not differentiable projection operator that is deemed unsuitable for designing ZNN solvers in the community, due to the lack of the required time derivative information. To achieve the aforementioned aim, the upper right-hand Dini derivative of the projection operator with respect to its input is introduced to serve as a mode switcher, leading to a novel ZNN solver, termed Dini-derivative-aided ZNN (Dini-ZNN). In theory, the convergent optimal solution of the Dini-ZNN solver is rigorously analyzed and proved. Comparative validations are performed, verifying the effectiveness of the Dini-ZNN solver that has merits such as guaranteed capability to solve problems, high solution accuracy, and no extra hyperparameter to be tuned. To illustrate potential applications, the Dini-ZNN solver is successfully applied to kinematic control of a joint-constrained robot with simulation and experimentation conducted.

Hierarchical attitude stabilization controller design and analysis for underactuated spacecraft on SO(3)

In this paper, the complete attitude stabilization of underactuated spacecraft with two parallel single gimbal control moment gyroscopes (SGCMGs) is explored on 3-dimensional special orthogonal group (SO(3)) and its corresponding Lie algebra (so(3)). Instead of assumption on zero total angular momentum in existing article, a less conservative criterion is adopted to ensure system controllability, which does not simplify system dynamics. On the kinematic level, an ideal controller is proposed to globally stabilize attitude without singular initial condition and stagnation behavior. Besides, the ideal kinematic controller is developed on so(3), which avoids singularity or unwinding phenomenon caused by other attitude parameters. On the dynamic level, a hierarchical controller consisting of two parts is proposed. The high-level sliding mode part enforces finite time stabilization of angular velocity about underactuated axis while the low-level part tracks ideal angular velocity commands about actuated axes. A rigorous proof of the whole dynamic-kinematic system that has not been explored before is given. Analysis of hierarchical control from the perspective of linear algebra provides a novel insight into controller design for underactuated systems. Furthermore, the paradigm of controller design which is applicable to other underactuated systems is derived. Simulation results validate the effectiveness of the proposed control logic.

Safety-critical anti-disturbance control of tugs for collaborative berthing

It is a challenging task for large ships themselves berthing in port areas even for an experienced captain. This paper addresses the collaborative berthing by utilizing tugs maneuvering at low speeds for operating a ship. A safety-critical anti-disturbance control method is proposed for collaborative berthing subject to the contact force constraint, velocity constraints, ship constraints referring to constraining the tugs velocities in the body frame of the ship and the angular rate of the ship, as well as ocean disturbances. First, a finite-time kinematic control law is developed for each tug to track the desired position and heading prescribed by using the relative distance between two tugs and the heading of port shoreline. Next, an extended state observer (ESO) based on robust exact differentiator (RED) is used for each tug to estimate the model uncertainties and environment disturbances. Then, a safety-critical anti-disturbance control law is designed for each tug by employing an RED-based ESO. Finally, through converting the contact force constraint and ship constraints to velocity constraints on tugs, a quadratic programming problem is solved to obtain the collaborative velocity signals for achieving the collaborative operation of the connected tugs subject to velocity constraints. It is proven that the closed-loop control system is finite-time input-to-state stable and the safety during collaborative berthing can be guaranteed in the presence of environmental disturbances. Simulation results are given to substantiate the efficacy of the proposed safety-critical anti-disturbance control method of tugs for collaborative berthing.

Second-Order Terminal Sliding Mode Control for Trajectory Tracking of a Differential Drive Robot

This paper proposes a second-order terminal sliding mode (2TSM) approach to the trajectory tracking of the differential drive mobile robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the innermost loop, tracks the robot’s velocity quantities while a kinematic controller, at the outermost loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated, and finite-time convergence is guaranteed by the second-order TSM manifold, which involves higher-order derivatives of the state variables, resulting in an inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

Adaptive immune fuzzy quasi-sliding mode control for leader–follower formation of wheeled mobile robots under uncertainties and disturbances with obstacle avoidance

PurposeThis paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.Design/methodology/approachTo deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.FindingsTo verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.Originality/valueThe proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

Time-optimal constrained kinematic control of robotic manipulators by recurrent neural network

Time-optimal kinematic control is a vital concern for industrial manipulators to save allocated motion task time as much as possible. This requires maximizing the end-effector velocity to minimize the time required for path tracking. Nonetheless, it remains a challenge to ensure that joint motion constraints are not violated during this process, even with the aim of maximizing end-effector velocity simultaneously. This paper introduces a novel approach, which for the first time leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to attain time-optimal kinematic control for manipulators. The theoretical analysis of the RNN-based kinematic control solver is addressed, ensuring both its optimality and convergence for achieving time-optimal kinematic control. The proposed method enables the maximization of end-effector velocity to achieve time-optimal kinematic control without violating all joint velocity limits simultaneously. In contrast to previous kinematic control schemes, the proposed method can enhance the end-effector path tracking speed of completion by 100% around, we substantiate the effectiveness and superiority of the proposed approach via simulation and V-Rep experiment on the manipulators.