Robot Control Problem Research Articles

Decoupled Intensity-Based Nonmetric Visual Servo Control

This brief addresses the problem of vision-based robot control where the equilibrium state is defined via a goal image. Specifically, we consider the class of intensity-based nonmetric solutions, which provide for high accuracy, versatility, and robustness. Existing techniques within that class present a fully coupled translational and rotational control error dynamics, what increases analysis complexity and may degrade system performance. This brief proposes a new intensity-based nonmetric visual servoing technique that decouples the translational control error dynamics, regardless of the observed object characteristics, camera displacements, and their relative poses. The obtained system is thus lower triangular in the general case. For some practical cases, the proposed general technique leads to the Grail of a fully decoupled (i.e., strictly diagonal) linear dynamics. The theoretical analysis of local diffeomorphism, local exponential stability, and those decoupling properties is provided. Improved performances are also experimentally confirmed using a 6-DoF robotic arm in various positioning and tracking tasks.

Visual Trajectory Tracking of Wheeled Mobile Robots With Uncalibrated Camera Extrinsic Parameters

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach.

Computational Modeling of Emotion-Motivated Decisions for Continuous Control of Mobile Robots

Immediate rewards are usually very sparse in the real world, which brings a great challenge to plain learning methods. Inspired by the fact that emotional reactions are incorporated into the computation of subjective value during decision-making in humans, an emotion-motivated decision-making framework is proposed in this article. Specifically, we first build a brain-inspired computational model of amygdala–hippocampus interaction to generate emotional reactions. The intrinsic emotion derives from the external reward and episodic memory and represents three psychological states: 1) valence; 2) novelty; and 3) motivational relevance. Then, a model-based (MB) decision-making approach with emotional intrinsic rewards is proposed to solve the continuous control problem of mobile robots. This method can execute online MB control with the constraint of the model-free policy and global value function, which is conducive to getting a better solution with a faster policy search. The simulation results demonstrate that the proposed approach has higher learning efficiency and maintains a higher level of exploration, especially, in some very sparse-reward environments.

Consensus Control for Wheeled Mobile Robots Under Input Saturation Constraint

In this article, the consensus control problem for the nonholonomic wheeled mobile robots under the input saturation constraint is addressed. The specified-time observer is designed to estimate the root node’s linear speed and angular speed by only using the configuration of the robot, and the convergence time when the observer obtains the real values can be specified in advance according to the specific task requirements. Based on the observed velocity states, the consensus controller subjects to input saturation is constructed to steer a wheeled mobile robot to keep track of the root node. Last, to verify the feasibility and effectiveness of the proposed control strategy, both the numerical simulation and physical experiment results are conducted.

Practical Inverse Kinematic Solution Based on Calibrated Parameters Using in Engineering

Inverse kinematic (IK) problem is an essential problem in industrial robot control. In some high accuracy projects, the solution needs the robot act based on online sampled data rather than the pre-teached actions. It is necessary to reduce the absolute positioning error to complete these challenging projects. The IK solution is on the way there. This article simply introduces the standard IK solution based on robot KUKA R2700, points out the shortage of standard IK solution in enhancing the absolute positioning accuracy and develops an practical IK solution, PIKFCP, which is necessary in some measures.

Modification of gesture-determined-dynamic function with consideration of margins for motion planning of humanoid robots

The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for the GDDF, which causes a lack of adaptability to the practical applications of this method. In this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is proposed. This MGDDF scheme is based on quadratic programming (QP) framework, which is widely applied to solving the redundancy resolution problems of robot arms. Moreover, three margins are introduced in the proposed MGDDF scheme to avoid joint limits. With consideration of these margins, the joints of manipulators of the humanoid robots will not exceed their limits, and the potential damages which might be caused by exceeding limits will be completely avoided. Computer simulations conducted on MATLfurther verify the feasibility and superiority of the proposed MGDDF scheme.

A stable analytical solution method for car-like robot trajectory tracking and optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.

Adaptive locomotion control system for robots with arbitrarily modular design

Abstract The paper proposes an adaptive control system for modular hyper-redundant systems that can learn to solve the control problem of robots with arbitrary designs from a given class. The proposed model uses logical-probabilistic knowledge discovery methods to find effective control patterns in an array of system’s environment interaction statistical data. For making the system independent on the chosen robot design, it was proposed to include design information in the training data. Using this information during the training process allows the control system to tune in to control the robot, regardless of its design. The effectiveness of the approach is demonstrated by the example of training virtual models of robots to move forward.

Online optimization based adaptive tracking control for redundant manipulators with model uncertainties

Tracking control of robot manipulators is always a fundamental problem in robot control, especially for redundant manipulators with higher DOFs. This problem may become more complicated when there exist uncertainties in the robot model. In this paper, we propose an adaptive tracking controller considering the uncertain physical parameters. Based on the coordinate feedback, a Jacobian adaption strategy is firstly built by updating kinematic parameters online, in which neither cartesian velocity nor joint acceleration is required, making the controller much easier to built. Using the Pseudo-inverse method of Jacobian, the optimal repeatability solution is achieved as the secondary task. Using Lyapunov theory, we have proved that the tracking errors of the end-effector asymptotically converge to zero. Numerical simulations are provided to validate the effectiveness of the proposed tracking method.

Calibration-Free Image-Based Trajectory Tracking Control of Mobile Robots With an Overhead Camera

To make the controller implementation easier and to enhance the system robustness and control performance in the presence of the camera parameter uncertainties, it is very desired to develop vision-based control approaches without any offline or online camera calibration. In this article, we propose a new calibration-free image-based trajectory tracking control scheme for nonholonomic mobile robots with a truly uncalibrated fixed camera. By developing a novel camera-parameter-independent kinematic model, both offline and online camera calibration can be avoided in the proposed scheme, and any knowledge of the camera is not needed in the controller design. The proposed trajectory tracking control scheme can guarantee exponential convergence of the image position and velocity tracking errors. To illustrate the performance of the proposed scheme, experimental results are provided in this article. Note to Practitioners —This article was motivated by the vision-based motion control problem of mobile robots in uncalibrated environments. Existing vision-based motion control approaches for nonholonomic mobile robots generally depend on offline precise/coarse or online numerical/adaptive calibration of the camera intrinsic and extrinsic parameters and require precise or coarse knowledge of the camera in their implementation. This article presents a novel calibration-free image-based trajectory tracking control scheme, which can be implemented easily in real environments without any offline or online calibration of the camera parameters and can be used to efficiently control the motion of nonholonomic mobile robots with an arbitrarily placed and truly unknown overhead camera. Experimental results show that the proposed scheme can achieve satisfactory trajectory tracking control performance despite the lack of any knowledge about the camera intrinsic and extrinsic parameters and the presence of unknown camera lens distortions, and hence, can provide a simple but efficient solution to the vision-based motion control problem of nonholonomic mobile robots.

Multi-surface sliding mode control of continuum robots with mismatched uncertainties

In this paper, we tackle the control problem of continuum robots with mismatched uncertainties. Uncertainties that affect systems through any of their states and may not be directly accessed by their controllers. These uncertainties emerge in a system either due to unmodeled dynamics, practical limitations, or external disturbances. Continuum robots possess highly nonlinear dynamic behaviour due to their elastic nature and operate within undefined or congested environments, exposing them to such uncertainties. However, mismatched uncertainties in the continuum robots’ field, are yet to be addressed. Here, we tackle this problem and propose the first robust control for continuum robots that assures its robustness property under mismatched uncertainties. To this end, we first derive the dynamic model for our continuum robot by considering it as an elastic rod and then applying Cosserat rod theory. This will result in a general dynamic model that does not require any design or operative assumption. Next, we design our robust controller utilizing multi-surface sliding mode control, a method capable of handling nonlinear systems under mismatched uncertainties. Finally, we include simulations to validate our controller’s performance.

Dynamic running hexapod robot based on high-performance computing

Advanced high-performance computing (HPC) plays an important role in solving complex and large problems in robot simulation, parameter identification, and control. This article introduces a flexible hexapod robot with arc-shaped legs that has the ability to move fast and agilely based on HPC. Its mechanical structure design follows the principle of miniaturization and lightweight. While the robot uses drive devices with high power density, power supply, and drive, systems with high performance are designed for it to meet the requirements of high bursting capabilities and rapid movement capabilities. Meanwhile, this article proposes a gait generation and control method for the hexapod robot based on max-plus algebra. This method regards the movement of touching and leaving the ground during robot’s walking as discrete events and uses a set of max-plus algebraic linear equations to describe the sequence of each discrete event in different locomotion gaits. By using this method, control laws for various gaits can be generated easily, switch of different gaits in real time can be achieved by switching the control matrix of these gaits, while the stability of the switching process and the synchronism of the locomotion of each leg before and after switching are also ensured. The virtual prototype simulation and physical prototype experiments were exerted to verify the effectiveness of the structure design and control method. Moreover, the simulation analysis and prototype experiment of Trotting diagonal gait and Pronking jumping gait were carried out in which the Pronking gait had a maximum traveling speed of 1.2 m/s.

Different-level algorithms for control of robotic systems

• The different-level nonlinear system is first investigated. • The zeroing equivalency between different levels is proposed. • The new algorithm is proposed based on the equivalency and a discretization formula. • The problem is solved in a discrete-time manner with the future-instant solution predicted. • The problem of robotic system control with the end-effector orientation and fault tolerance considered simultaneously is solved. In this paper, we consider a special kind of time-dependent nonlinear system that includes different-level subsystems (i.e., a subsystem with respect to time-dependent solution and a subsystem with respect to its time derivative). To solve this kind of time-dependent nonlinear system, an equivalency between different-level subsystems is proposed and termed as zeroing equivalency based on the zeroing neural network method. This kind of time-dependent nonlinear system can be solved in a discrete-time manner with future-instant solution predicted at current instant based on the zeroing equivalency and a discretization formula. Prediction ability can perfectly meet the requirements of real-time computation, which is crucial in engineering fields. In addition, different-level algorithms are further employed to solve the problem of robotic system control considering additional restrictions. The position tracking control and end-effector orientation control are simultaneously achieved with even fault tolerance by utilizing the proposed algorithms.

A Practical Leader–Follower Tracking Control Scheme for Multiple Nonholonomic Mobile Robots in Unknown Obstacle Environments

This brief addresses the leader–follower (L-F) tracking control problem for multiple nonholonomic mobile robots in unknown obstacle environments. Unlike most of the existing approaches investigating similar problems, a series of practical issues is considered and tackled in the proposed scheme. For leader tracking, a class of bounded barrier functions are employed to formulate distance and bearing angle constraints introduced by sensor limitations and L-F collision avoidance requirement. To ensure robot safety in unknown environments, a multiregion obstacle avoidance algorithm is proposed which prioritizes different control objectives in different regions. This brief also studies the leader-loss situation, which may be caused by illumination variation, motion blurring, or visual occlusion by obstacles. To deal with this case, a fault-tolerant strategy is designed to drive $F$ to the place where $L$ was lost immediately. The control scheme proposed in the brief is primarily designed for a communication-free environment where only local state measurements are available. Furthermore, it has control input constraints explicitly taken into account. Real robot experiment has been performed to validate the proposed method.

Model Predictive Tracking Control of Nonholonomic Mobile Robots With Coupled Input Constraints and Unknown Dynamics

This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints. In order to handle kinematic constraints, the tube-based MPC scheme is introduced, which includes the state feedback controller to suppress the external disturbance in the velocity level. The tube-based MPC is transformed to a constrained quadratic programming (QP) problem, and then the QP problem can be efficiently solved by a primal-dual neural network over a finite receding horizon so as to obtain the optimal control velocity. Besides, an adaptive controller employing the neural network technology is proposed to acquire the approximation of the uncertain robotic dynamics. Moreover, an auxiliary control is developed in order to deal with actuator saturation, and a disturbance observer is designed to reject the external disturbance online in the dynamic level. Subsequently, through Lyapunov function synthesis, the stability of the closed-loop system have been guaranteed. Finally, in order to verify the effectiveness, the experimental studies are carried out using an actual mobile robot.

Least-squares policy iteration algorithms for robotics: Online, continuous, and automatic

Reinforcement learning (RL) is a general framework to acquire intelligent behavior by trial-and-error and many successful applications and impressive results have been reported in the field of robotics. In robot control problem settings, it is oftentimes characteristic that the algorithms have to learn online through interaction with the system while it is operating, and that both state and action spaces are continuous. Least-squares policy iteration (LSPI) based approaches are therefore particularly hard to employ in practice, and parameter tuning is a tedious and costly enterprise. In order to mitigate this problem, we derive an automatic online LSPI algorithm that operates over continuous action spaces and does not require an a-priori, hand-tuned value function approximation architecture. To this end, we first show how the kernel least-squares policy iteration algorithm can be modified to handle data online by recursive dictionary and learning update rules. Next, borrowing sparsification methods from kernel adaptive filtering, the continuous action-space approximation in the online least-squares policy iteration algorithm can be efficiently automated as well. We then propose a similarity-based information extrapolation for the recursive temporal difference update in order to perform the dictionary expansion step efficiently in both algorithms. The performance of the proposed algorithms is compared with respect to their batch or hand-tuned counterparts in a simulation study. The novel algorithms require less prior tuning and data is processed completely on the fly, yet the results indicate that similar performance can be obtained as by careful hand-tuning. Therefore, engineers from both robotics and AI can benefit from the proposed algorithms when an LSPI algorithm is faced with online data collection and tuning by experiment is costly.

Arbitrary point-to-point stabilization control in specified finite time for wheeled mobile robots based on dynamic model

This paper investigates the arbitrary point-to-point stabilization control problem for wheeled mobile robots based on the second-order dynamics. The arbitrary point-to-point stabilization means the robot can be stabilized from any initial configuration to any other desired configurations. In this paper, at first, a global and asymptotical stabilization control law is presented directly based on the dynamic model, which can drive the robot to the identity configuration from any initial condition. Then, assisted by a new converted system, an arbitrary point-to-point stabilization control strategy is proposed, in which the initial and desired configurations are both arbitrarily chosen. Next, by means of a time-rescaling approach, a specified finite-time stabilization control law is derived from the asymptotical controller. In particular, the finite-time moment can be specified in advance. Finally, numerical simulations are presented to demonstrate the effectiveness of the control law.

Distributed sensing for fluid disturbance compensation and motion control of intelligent robots

A control methodology for aerial or aquatic vehicles is presented that leverages intelligent distributed sensing inspired by the lateral line found in fish to directly measure the fluid forces acting on the vehicle. As a result, the complex robot control problem is effectively simplified to that of a rigid body in a vacuum. Furthermore, by sensing these forces, they can be compensated for immediately, rather than after they have displaced the vehicle. We have created a sensory shell around a prototype autonomous underwater vehicle, derived algorithms to remove static pressure and calculate total force from the discrete measurements using a fitting technique that filters sensor error, and validated the control methodology on a vehicle in the presence of multiple fluid disturbances. This sensing control scheme reduces position tracking errors by as much as 72% compared to a standard position error feedback controller. Accurate manoeuvring of autonomous aerial and aquatic robots requires detailed knowledge of the fluid forces, which can be challenging especially in turbulent water or air. A control method for autonomous underwater vehicles (AUVs) uses intelligent distributed sensing inspired by fish ‘lateral line’ sensing. This is used by many species of fish to feel the flow around them and respond instantly, before they are displaced by disturbances. An AUV designed with such a sensory shell similarly compensates for disturbances and has improved position tracking.

Neuroadaptive Cooperative Control Without Velocity Measurement for Multiple Humanoid Robots Under Full-State Constraints

This paper studies the cooperative control problem of multiple humanoid robots handling a common payload in the presence of position and velocity constraints, unmeasurable velocity, as well as nonparametric uncertainties. By using a state observer to estimate the unmeasured velocity, a neuroadaptive output-feedback control scheme is developed, which by blending an error transformation with barrier Lyapunov function ensures that the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a preassignable convergence rate. Furthermore, it is shown that all the signals in the closed-loop system are ultimately semiglobally uniformly bounded. Simulation results are verified to show the effectiveness and benefits of the proposed scheme.

Dynamic Modeling of Three Links Robot Manipulator (Open Chain) with Spherical Wrist

Dynamic modeling of a robot manipulator is a central problem in an accurate robot control. In this paper; the dynamic equations of motion were derived by using Eular-Lagrange method for a six degree of freedom articulated robot manipulator based on the geometrical jacobian construction for each link and actuator. In addition, friction effects beside the end effector forces that act the environment are considered. A Matlab Simulink plant is developed to embrace the theoretical work and simulate the dynamic response for a designed nonlinear controller Proportional Derivative plus Gravity (PD+G), also a modified controller is applied to reject the disturbances and the internal friction effect where the settling errors were 3.57E-6, 2.09E-7, -3.63E-6, 8.84E-6, -5.39E-8 and -4.39E-5 (deg) for joints one to six respectively. The presented approach can be applicable to solve the dynamic problem of other n-link robot manipulators and achieve a suitable solution for tracking trajectories.