Robot Control Problem Research Articles

Physical reservoir computing with origami and its application to robotic crawling

A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir’s physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami’s physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence.

Distributed Formation Control of Nonholonomic Wheeled Mobile Robots Subject to Longitudinal Slippage Constraints

This paper investigates the distributed formation control problem of multiple nonholonomic wheeled mobile robots within real environments. The formation pattern of the system adopts leader–follower structure and the communication topology among the multirobot system is modeled by a directed graph. Slippage is hard to avoid due to the possible existence of ice, sand, or muddy roads. To overcome the effect of slippage, an adaptive trajectory tracking controller for leader robot is designed, such that the leader robot can keep up with the virtual reference trajectory and estimate the real value of unknown slipping ratio accurately. In addition, it is difficult for each follower robot to obtain leader’s states in a large formation system, so distributed formation controllers are designed based on distributed observers. It is shown that the proposed controllers can realize formation objective and overcome the slippage constraints at the same time. Finally, the effectiveness of the proposed controllers is verified by simulation results.

Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.

A Weighted Method for Fast Resolution of Strictly Hierarchical Robot Task Specifications Using Exact Penalty Functions

Extensive work has been done on efficiently resolving hierarchical robot task specifications that minimize the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -2 norm of linear constraint violations, but not for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -1 norm, in which there has recently been growing interest for sparse control. Both approaches require solving a cascade of quadratic programs (QP) or linear programs (LP). In this letter, we introduce alternate and more efficient approaches to hierarchical <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -1 norm minimization by formulating it as a single LP that can be solved by any off-the-shelf solver. The first approach is a recursive method that transforms the lexicographic LP (LLP) into a single objective problem using Lagrangian duality. The second approach, which forms the main focus of this letter, is a weighted method based on the exact penalty method, that is equivalent to the original LLP for a well chosen set of weights. We propose methods to compute and adapt these weights. The algorithms were applied on an interesting dual arm robot task. We discuss and benchmark the computational efficiency of these methods. Simplicity of the weighted method makes it a promising approach for tackling challenging prioritized robot control problems involving a control horizon or nonlinear constraints. Within this letter, we take the first step towards that goal by demonstrating the efficacy of the weighted method on a simpler instantaneous robot control problem with linear constraints.

Event‐driven adaptive near‐optimal tracking control of the robot in aircraft skin inspection

AbstractIn this article, we discuss a near‐optimal tracking control problem (NOTCP) of robots used for inspecting aircraft skin with partially unknown systems, unmeasurable states, unknown disturbances, and unknown output delay. A novel observer based on an augmented neural network is designed to overcome the unknown disturbances, unknown output delay, and unknown internal states. An augmented system state, composed of the tracking error and reference system state, is proposed to introduce a new nonquadratic discounted performance function for the NOTCP. Due to the complexity in solving the Hamilton–Jacobi–Bellman equation, an online policy iteration is presented under the adaptive dynamic programming (ADP) framework. Unlike the traditional ADP, the event‐driven algorithm updates the control input only when the event is triggered, which reduces the computational cost and transmission load. Both the control policy and the observer are updated according to the developed triggering condition. Convergence to a near‐optimal control solution and the stability analysis of the proposed algorithm are shown through the Lyapunov candidate function for both the continuous and jump dynamics. The performance of the proposed algorithm is demonstrated by simulation.

Factor Graphs: Exploiting Structure in Robotics

Many estimation, planning, and optimal control problems in robotics have an optimization problem at their core. In most of these optimization problems, the objective to be maximized or minimized is composed of many different factors or terms that are local in nature—that is, they depend only on a small subset of the variables. A particularly insightful way of modeling this locality structure is to use the concept of factor graphs, a bipartite graphical model in which factors represent functions on subsets of variables. Factor graphs can represent a wide variety of problems across robotics, expose opportunities to improve computational performance, and are beneficial in designing and thinking about how to model a problem, even aside from performance considerations. I discuss each of these three aspects in detail and review several state-of-the-art robotics applications in which factor graphs have been used with great success.

An optimal property of the hyperplane system in a finite cubing

Motivated by navigation and control problems in robotics, Ghrist and Peterson introduced a class of non-positively curved (NPC) cubical complexes arising as configuration spaces of reconfigurable systems, best regarded as discretized state space representations of embodied agents such as a multi-jointed robotic arm. In current real world applications, agents are increasingly required to respond autonomously to sensory input in order for them to contend with a priori unknown obstacles to navigation. In particular, the configuration spaces in question may not be known in advance. This motivates the following problem formulation: Given a NPC cubical complex $$\mathcal {C}$$ and a point-separating collection $$\varSigma $$ of Boolean queries on its 0-skeleton, $$\mathcal {C}^{(0)}$$ , find an efficient algorithm for learning $$\mathcal {C}$$ from the outputs provided by $$\varSigma $$ along an appropriately chosen path in $$\mathcal {C}$$ . In this note, we tackle the problem of identifying $$\mathcal {C}$$ when it is known that $$\mathcal {C}$$ is CAT(0). We show that the collection of canonical hyperplanes of $$\mathcal {C}$$ is the unique solution of a sub-modular minmax problem over the space of point-separating systems of Boolean queries on $$\mathcal {C}^{(0)}$$ , which may also be formulated in terms of the quadratic form associated with the graph Laplacian of $$\mathcal {C}^{(1)}$$ .

The principle of dividing feasible trajectories in a robot control problem

The problem of control of mobile robot is considered. It is assumed that the start and end points of the trajectory are set, and one or more obstacles in the form of circles are present on the line connecting these points. Circles are defined by the coordinates of their centers and the lengths of their radius. The feasible trajectory must not share points with the interiors of these circles. The proposed principle is to represent any feasible trajectory as a sequence of straight sections and arcs of circles that limit circular obstacles. The robot’s movement along straight sections is described by a second-order differential equation, and the movement along arcs is uniform with a given minimum speed.

Voice frequency impact for voice control of collaborative robots

The project is focused on research of voice control of collaborative robots. Previous research of voice control and speech recognition research have given examples and evidence that machines can truly be controlled by human voice. The main problem in the voice control of collaborative robots is the lack of reliability and safety of control compared to manual control. Of the results achieved, the most and least relevant voice control problem has been clearly identified, next step is voice frequency impact for voice control. The experiments were performed in the robotics laboratory at the Faculty of Materials Science and Technology in Trnava, and this using an Arduino kit and a small robot “DOBOT Magician“. In the final part of the project are summarized all experimentally found facts and confronted with the assumptions set in the design of the experiment.

A Spectral RMIL+ Conjugate Gradient Method for Unconstrained Optimization With Applications in Portfolio Selection and Motion Control

The Spectral conjugate gradient (SCG) methods are among the efficient variants of CG algorithms which are obtained by combining the spectral gradient parameter and CG parameter. The success of SCG methods relies on effective choices of the step-size $\alpha _{k}$ and the search direction $d_{k}$ . This paper presents an SCG method for unconstrained optimization models. The search directions generated by the new method possess sufficient descent property without the restart condition and independent of the line search procedure used. The global convergence of the new method is proved under the weak Wolfe line search. Preliminary numerical results are presented which show that the method is efficient and promising, particularly for large-scale problems. Also, the method was applied to solve the robotic motion control problem and portfolio selection problem.

A Shared Control Strategy for Reach and Grasp of Multiple Objects Using Robot Vision and Noninvasive Brain–Computer Interface

It is ambitious to develop a brain-controlled robotic arm for some patients with motor impairments to perform activities of daily living using brain&#x2013;computer interfaces (BCIs). Despite much progress achieved, this mission is still very challenging mainly due to the poor decoding performance of BCIs. The problem is even exacerbated in the case of noninvasive BCIs. A shared control strategy is developed in this work to realize flexible robotic arm control for reach and grasp of multiple objects. With the intelligent assistance provided by robot vision, the subject was only required to finish gross reaching movement and target selection using a simple motor imagery-based BCI with binary output. Along with the user control, the robotic arm, which identified and localized potential targets within the workspace in the background, was capable of providing both trajectory correction in the reaching phase to reduce trajectory redundancy and autonomous grasping assistance in the phase of grasp. Ten subjects participated in the experiments containing one session of two-block grasping tasks with fixed locations and another one of randomly placed three-block grasping tasks. The results of the experiments demonstrated substantial improvement with the shared control system. Compared with the single BCI control, the success rate of shared control was significantly higher (<inline-formula> <tex-math notation="LaTeX">$p &lt; 0.001$ </tex-math></inline-formula> for group performance), and moreover, the task completion time and perceived difficulty were significantly lower (<inline-formula> <tex-math notation="LaTeX">$p &lt; 0.001$ </tex-math></inline-formula> for group performance both), indicating the potential of our proposed shared control system in real applications. <i>Note to Practitioners</i>&#x2014;This article is motivated by the problem of dexterous robotic arm control based on a brain&#x2013;computer interface (BCI). For people suffering from severe neuromuscular disorders or accident injuries, a brain-controlled robotic arm is expected to provide assistance in their daily lives. A primary bottleneck to achieve the objective is that the information transfer rate of current BCIs is not high enough to produce multiple and reliable commands during the online robotic control. In this work, machine autonomy is incorporated in a BCI-controlled robotic arm system, where the user and machine can work together to reach and grasp multiple objects in a given task. The intelligent robot system autonomously localized the potential targets and provided trajectory correction and grasping assistance accordingly. Meanwhile, the user only needed to complete gross reaching movement and target selection with a basic binary motor imagery-based BCI, which reduced the task difficulty and retained the volitional involvement of the user at the same time. The results of the experiments showed that the accuracy and efficiency of grasping tasks increased significantly in the shared control mode together with a significant decrease in the perceived mental workload, which indicates that our proposed shared control system is effective and user-friendly in practice. In the future, more feedback information will be introduced to enhance the task performance further, and a wheelchair-mounted robotic arm system will be developed for greater flexibility. In addition, more functional task modules (e.g., self-feeding and opening doors) should be integrated for more practical utilities.

Globally convergent leaderless formation control for unicycle‐type mobile robots

This study investigates the leaderless formation control problem for unicycle-type mobile robots and designs a formation controller for a two-robot system. The proposed control law is globally effective, namely making mobile robots converge to the formation from all kinds of initial conditions. The kinematic model of mobile robots is directly established on the special Euclidean group, and the mobile robot is merely required to obtain the relative configuration (i.e. position and orientation) in its own body-fixed frame. The approach for the formation problem is to design a virtual system for each mobile robot, which contains the desired formation pattern, so that the formation problem for mobile robots is converted into the consensus problem for virtual systems. Furthermore, the non-holonomic constraint for the virtual systems is derived for the first time, which is explained from the viewpoint of geometric control. In addition, the authors clarify the physical meaning of the multiple control gains, which can guarantee excellent robot behaviour by appropriate selection.

Event-based tracking control for nonholonomic mobile robots

In this paper, we study the event-triggered control problem for a kind of nonlinear time-varying system. Generally speaking we use the Lyapunov stability theory to solve an event-triggered tracking control problem of non-holonomic mobile robots. Firstly, we propose a continuous tracking control law using nonlinear state feedback. Under some proper assumptions, we use the linearization technique to prove the exponential stability of the tracking system. Then, we propose an event-triggered controller for the robot and prove the uniform boundedness of the solutions by using the Gronwall–Bellman inequality. Moreover, it is shown that the solutions are uniformly ultimately bounded and there is no Zeno behavior in the tracking system. Simulation studies show the effectiveness of the proposed approach.

A novel impedance control method of rubber unstacking robot dealing with unpredictable and time-variable adhesion force

Unpredictable and time-variable adhesion force between the rubber unstacking robot and the rubber block is generated, which makes it difficult for the robot to smoothly complete the rubber disassembly task, thereby bringing about new robot control problems. For solving the above problems, a novel method of inner/outer loop impedance control based on natural gradient actor-critic (NAC) reinforcement learning is proposed in this paper. The required impedance is applied by the inner/outer loop impedance control with time delay estimation, which can correct the modeling error and compensate the nonlinear dynamics term to improve the computational efficiency of the system. In addition, the NAC reinforcement learning algorithm based on recursive least squares filtering is used to optimize the impedance parameters online, which can improve the impedance accuracy and robustness in the unstructured dynamic environment. At the same time, three stability constraints of the control strategy are derived in the analysis process. Finally, by setting up the experimental platform, it is verified that the control strategy can make the robot work smoothly under the action of unpredictable and time-variable adhesion force to reduce vibration and improve rubber unstacking performance.

Distributed consensus-formation of force-controlled nonholonomic robots with time-varying delays

We solve the leaderless consensus-formation control problem for nonholonomic robots under the influence of time-varying communication delays and via smooth feedback. The control objective is to ensure that both the Cartesian positions of the vehicles and their orientations on the plane converge to a given position in a formation pattern, relatively to an a priori unknown barycenter and a common orientation, respectively. Our controller, being of proportional-plus-damping type and smooth time-varying, is easy to implement. Furthermore, it relies on δ-persistency of excitation to overcome the well-known obstacle that for nonholonomic systems a set-point is not stabilizable by smooth time-invariant feedback. We establish asymptotic convergence of the tracking errors and we provide some simulation results that support our theoretical findings.

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Flocking control problem of mobile robots under environment with unknown obstacles is addressed in this paper. Based on the simulated annealing algorithm, a flocking behaviour for mobile robots is achieved which converges to alignment while avoiding obstacles. Potential functions are designed to evaluate the positional relationship between robots and obstacles. Unlike the existing analytical method, simulated annealing algorithm is utilized to search the quasi-optimal position of robots in order to reduce the potential functions. Motion control law is designed to drive the robot move to the desired position at each sampling period. Experiments are implemented, and the results illustrate the effectiveness of the proposed flocking control method.

Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks

Purpose of research. The present paper conserns the problem of using reaction predictors in the control system of bipedal walking robots. The main advantage of using predictors is the ability to exclude unknown reaction forces from the dynamics equations and, consequently, from the robot control problem statements based on the model. An additional advantage of predictor setting of control tasks is also discussed in the paper, namely the possibility of its use to predict changes in contact interaction modes, such as slipping motion or foot lifting from the supporting surface.Methods. The following methods are used in the research: the method of dynamics of multi-mass systems is necessary for developing a mathematical model of the behavior of a walking robot and describing its contact interaction with the support surface, the method of neural networks is used to develop a predictor that allows one to forecast the values of reactions between the robot’s foot and the surface.Results. The paper shows that there is a connection between the frequencies of the harmonic components of robot movements (the ratio p of these frequencies in the experiment and the training sample) and the quality of reactions predictor operation of the support surface. This indicates the importance of applying a representative spectrum of walking robot movement frequencies in forming a training sample, and the poor generalizability of the predictor in relation to movement frequency.Conclusion. The paper has considered the use of a reaction predictor to identify the possibility of changing the mode of contact interaction, based on the measurement of discrepancies between local linearizations for various discrete steps. The results obtained in this work will be used in the development of a motion control system for a bipedal walking robot, which allows the device to adapt to the parameters of the support surface on which the movement occurs.

Physical human–robot interaction force control method based on adaptive variable impedance

This study presents a method for extracting the actual interaction force from a force sensor signal by measuring the gravity of the end-effector. The variable impedance control problem of commercial industrial robots is studied, to investigate the uncertain contact impedance characteristics between the human palm and a robot end-effector in the physical human-robot interaction process. A speed-based variable impedance adaptive interaction control method is proposed. The damping parameters of the admittance controller are adaptively adjusted according to the interactive force tracking error. The desired speed of the robot is also given, which is tracked in the Cartesian space speed mode of a Stäubli TX-90 robot to achieve interactive force control. An experimental comparison is made between the control strategies of the constant impedance and adaptive variable impedance. The experimental results show that the proposed method is effective.

Distributed cooperative control for nonholonomic wheeled mobile robot systems

The purpose of this paper is two-fold. First, the smoothness of the distributed consensus control (DCC) protocols of multi-agent systems (MAS) with inherent nonlinear dynamics is investigated. Different from the traditional consensus protocols where variable structure control is considered for robust control, a continuous DCC is presented to achieve consensus tracking without control chattering. Using certain types of exponential functions, it is shown that different smooth DCC schemes can be constructed to achieve asymptotic consensus tracking with smooth control input for MAS with connected directed/undirected and fixed/switched communication topologies. Second, the formation control problem of nonholonomic wheeled mobile robots (WMR) is solved via the continuous DCC. By converting the formation problem into consensus problem using the agents’ kinematics, the formation speed-control problem in planar motion is formulated and solved. The translational and rotational speeds inputs to the WMR are computed using continuous distributed consensus protocols to reduce chattering in the control inputs of mobile robots. The distinctive feature of the proposed consensus and formation algorithms is the high tracking precision with smooth control input to agents’ dynamics. In simulation studies, different numerical examples are illustrated to demonstrate the performance and effectiveness of the proposed DCC.

Angle-Constrained Formation Control for Circular Mobile Robots

In this letter, we investigate the formation control problem of mobile robots moving in the plane where, instead of assuming robots to be simple points, each robot is assumed to have the form of a disk with equal radius. Based on interior angle measurements of the neighboring robots' disk, which can be obtained from low-cost vision sensors, we propose a gradient-based distributed control law and show the exponential convergence property of the associated error system. By construction, the proposed control law has the appealing property of ensuring collision avoidance between neighboring robots. We also present simulation results for {a team} of four circular mobile robots forming a rectangular shape.