A review of mobile robots: Concepts, methods, theoretical framework, and applications

Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human–robot interfaces, safe human–robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.

A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications

Recently, Recurrent Neural Network (RNN) control schemes for redundant manipulators have been extensively studied. These control schemes demonstrate superior computational efficiency, control precision, and control robustness. However, they lack planning completeness. This paper explains why RNN control schemes suffer from the problem. Based on the analysis, this work presents a new random RNN control scheme, which 1) introduces randomness into RNN to address the planning completeness problem, 2) improves control precision with a new optimization target, 3) improves planning efficiency through learning from exploration. Theoretical analyses are used to prove the global stability, the planning completeness, and the computational complexity of the proposed method. Software simulation is provided to demonstrate the improved robustness against noise, the planning completeness and the improved planning efficiency of the proposed method over benchmark RNN control schemes. Real-world experiments are presented to demonstrate the application of the proposed method.

Quadrupedal Robots Whole-Body Motion Control Based on Centroidal Momentum Dynamics

In this paper, we demonstrate a method for quadruped dynamic locomotion based oncentroidal momentum control. Our method relies on a quadratic program that solves an optimalcontrol problem to track the reference rate of change of centroidal momentum as closely as possiblewhile satisfying the dynamic, input, and contact constraints of the full quadruped robot dynamics.Given the desired footstep positions, the according reference rate of change of the centroidalmomentum is formulated as a feedback control task derived from the CoM motions of a simplifiedmodel (linear inverted pendulum) based on Capture Point dynamics. The joint accelerations and theGround Reaction Forces(GRFs) outputted from the quadratic program solver are used to calculatethe desired joint torques using an inverse dynamics algorithm. The performance of the proposedmethod is tested in simulation and on real hardware.

Water Sink Model for Robot Motion Planning

There are various motion planning techniques for robots or agents, such as bug algorithm, visibility graph, Voronoi diagram, cell decomposition, potential field, and other probabilistic algorithms. Each technique has its own advantages and drawbacks, depending on the number and shape of obstacles and performance criteria. Especially, a potential field has vector values for movement guidance to the goal, and the method can be used to make an instantaneous and smooth robot movement path without an additional controller. However, there may be some positions with zero force value, called local minima, where the robot or agent stops and cannot move any further. There are some solutions for local minima, such as random walk or backtracking, but these are not yet good enough to solve the local minima problem. In this paper, we propose a novel movement guidance method that is based on the water sink model to overcome the previous local minima problem of potential field methods. The concept of the water sink model is to mimic the water flow, where there is a sink or bathtub with a plughole and floating piece on the water. The plughole represents the goal position and the floating piece represents robot. In this model, when the plug is removed, water starts to drain out via the plughole and the robot can always reach the goal by the water flow. The water sink model simulator is implemented and a comparison of experimental results is done between the water sink model and potential field.

Kinematically redundant actuators, a solution for conflicting torque–speed requirements

Robots often switch from highly dynamic motion to delivering high torques at low speeds. The actuation requirements for these two regimes are very different. As a consequence, the average efficiency of the actuators is typically much lower than the efficiency at the optimal working point. A potential solution is to use multiple motors for a single motor joint. This results in a redundant degree of freedom, which can be exploited to make the system more efficient overall. In this work, we explore the potential of kinematically redundant actuators in dynamic applications. The potential of a kinematically redundant actuator with two motors is evaluated against a single-motor equivalent in terms of operating range, maximum acceleration, and energy consumption. We discuss how the comparison is influenced by the design of the actuator and the way how the power is distributed over the input motors. Our results support the idea that kinematically redundant actuators can resolve the conflicting torque–speed requirements typical of robots.

A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation

There have been significant interests and efforts in the field of impedance control on robotic manipulation over last decades. Impedance control aims to achieve the desired mechanical interaction between the robotic equipment and its environment. This paper gives the overview and comparison of basic concepts and principles, implementation strategies, crucial techniques, and practical applications concerning the impedance control of robotic manipulation. This work attempts to serve as a tutorial to people outside the field and to promote discussion of a unified vision of impedance control within the field of robotic manipulation. The goal is to help readers quickly get into the problems of their interests related to impedance control of robotic manipulation and to provide guidance and insights in finding appropriate strategies and solutions.

FAT-Based Robust Adaptive Control of Electrically Driven Robots in Interaction with Environment

This paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

Adaptive Control Design and Stability Analysis of Robotic Manipulators

In this paper, the author presents the adaptive control design and stability analysis of robotic manipulators based on two main approaches, i.e., Lyapunov stability theory and hyperstability theory. For the Lyapunov approach, the author presents the adaptive control of a 2-DOF (degrees of freedom) robotic manipulator. Furthermore, the adaptive control technique and Lyapunov theory are subsequently applied to the end-effector motion control and force control, as in most cases, one only considers the motion control (e.g., position control, trajectory tracking). To make the robot interact with humans or the environment, force control must be considered as well to achieve a safe working environment. For the hyperstability approach, a control system is developed through integrating a PID (proportional–integral–derivative) control system and a model reference adaptive control (MRAC) system, and also the convergent behavior and characteristics under the situation of the PID system, model reference adaptive control system, and PID+MRAC control system are compared.

Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner ([Formula: see text], where [Formula: see text] is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

Active Impedance Control of Bioinspired Motion Robotic Manipulators: An Overview

There are two main categories of force control schemes: hybrid position-force control and impedance control. However, the former does not take into account the dynamic interaction between the robot's end effector and the environment. In contrast, impedance control includes regulation and stabilization of robot motion by creating a mathematical relationship between the interaction forces and the reference trajectories. It involves an energetic pair of a flow and an effort, instead of controlling a single position or a force. A mass-spring-damper impedance filter is generally used for safe interaction purposes. Tuning the parameters of the impedance filter is important and, if an unsuitable strategy is used, this can lead to unstable contact. Humans, however, have exceptionally effective control systems with advanced biological actuators. An individual can manipulate muscle stiffness to comply with the interaction forces. Accordingly, the parameters of the impedance filter should be time varying rather than value constant in order to match human behavior during interaction tasks. Therefore, this paper presents an overview of impedance control strategies including standard and extended control schemes. Standard controllers cover impedance and admittance architectures. Extended control schemes include admittance control with force tracking, variable impedance control, and impedance control of flexible joints. The categories of impedance control and their features and limitations are well introduced. Attention is paid to variable impedance control while considering the possible control schemes, the performance, stability, and the integration of constant compliant elements with the host robot.

Coupled and Decoupled Force/Motion Controllers for an Underwater Vehicle-Manipulator System

Autonomous interaction with the underwater environment has increased the interest of scientists in the study of control structures for lightweight underwater vehicle-manipulator systems. This paper presents an essential comparison between two different strategies of designing control laws for a lightweight underwater vehicle-manipulator system. The first strategy aims to separately control the vehicle and the manipulator and hereafter is referred to as the decoupled approach. The second method, the coupled approach, proposes to control the system at the operational space level, treating the lightweight underwater vehicle-manipulator system as a single system. Both strategies use a parallel position/force control structure with sliding mode controllers and incorporate the mathematical model of the system. It is demonstrated that both methods are able to handle this highly non-linear system and compensate for the coupling effects between the vehicle and the manipulator. The results demonstrate the validity of the two different control strategies when the goal is located at various positions, as well as the reliable behaviour of the system when different environment stiffnesses are considered.

On the coordination of highly dynamic human movements: an extension of the Uncontrolled Manifold approach applied to precision jump in parkour

The human body generally has more degrees of freedom than necessary for generating a given movement. According to the motor abundance principle, this redundancy is beneficial as it provides the central nervous system with flexibility and robustness for the generation of movements. Under the hypothesis of the Uncontrolled Manifold, the additional degrees of freedom are used to produce motor solutions by reducing the variability that affects the motion performance across repetitions. In this paper, we present a general mathematical framework derived from robotics to formulate kinematic and dynamic tasks in human movement. On this basis, an extension of the Uncontrolled Manifold approach is introduced to deal with dynamic movements. This extension allows us to present a complex experimental application of the proposed framework to highly dynamic task variables in parkour movements. This experiment involves dynamic tasks expressed in terms of linear and angular momenta. The results show that the central nervous system is able to coordinate such skilled tasks which appear to be preferentially controlled and hierarchically organized. The proposed extension is promising for the study of motion generation in anthropomorphic systems and provides a formal description to investigate kinematics and dynamics tasks in human motions.

Synthesis of Subject-Specific Human Balance Responses Using a Task-Level Neuromuscular Control Platform

Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closed-loop simulations to inform the design of the task-level controller. The predicted muscle activations and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction.

Optimization-Based Controllers for Robotics Applications (OCRA): The Case of iCub's Whole-Body Control

OCRA stands for Optimization-based Control for Robotics Applications. It consists of a set of platform-independent libraries which facilitates the development of optimization-based controllers for articulated robots. Hierarchical, weighted, and hybrid control strategies can easily be implemented using these tools. The generic interfaces provided by OCRA allow different robots to use the exact same controllers. OCRA also allows users to specify high-level objectives via tasks. These tasks provide an intuitive way of generating complex behaviors and can be specified in XML format. To illustrate the use of OCRA, an implementation of interest to this research topic for the humanoid robot iCub is presented. OCRA stands for Optimization-based Control for Robotics Applications. It consists of a set of platform-independent libraries which facilitates the development of optimization-based controllers for articulated robots. Hierarchical, weighted, and hybrid control strategies can easily be implemented using these tools. The generic interfaces provided by OCRA allow different robots to use the exact same controllers. OCRA also allows users to specify high-level objectives via tasks. These tasks provide an intuitive way of generating complex behaviors and can be specified in XML format. To illustrate the use of OCRA, an implementation of interest to this research topic for the humanoid robot iCub is presented.

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

This paper addresses the explicit force regulation problem for robot manipulators in interaction tasks. A new family of explicit force-control schemes is presented, which includes a term driven by a large class of saturated-type hyperbolic functions to handle the force error. Also, an active velocity damping term with the purpose of obtaining energy dissipation on the contact surface is incorporated plus compensation for gravity. In order to ensure asymptotic stability of the closed-loop system equilibrium point in Cartesian space, we propose a strict Lyapunov function. A force sensor placed at the end-effector of the robot manipulator is used in order to feed back the measure of the force error in the closed-loop, and an experimental comparison of the performanceL2-norm between 5 explicit force control schemes, which are the classical proportional-derivative (PD), arctangent, and square-root controls and two members of the proposed control family, on a two-degree-of-freedom, direct-drive robot manipulator, is presented.

A spiking neural model of adaptive arm control

We present a spiking neuron model of the motor cortices and cerebellum of the motor control system. The model consists of anatomically organized spiking neurons encompassing premotor, primary motor, and cerebellar cortices. The model proposes novel neural computations within these areas to control a nonlinear three-link arm model that can adapt to unknown changes in arm dynamics and kinematic structure. We demonstrate the mathematical stability of both forms of adaptation, suggesting that this is a robust approach for common biological problems of changing body size (e.g. during growth), and unexpected dynamic perturbations (e.g. when moving through different media, such as water or mud). To demonstrate the plausibility of the proposed neural mechanisms, we show that the model accounts for data across 19 studies of the motor control system. These data include a mix of behavioural and neural spiking activity, across subjects performing adaptive and static tasks. Given this proposed characterization of the biological processes involved in motor control of the arm, we provide several experimentally testable predictions that distinguish our model from previous work.

Task-Space Synchronization of Networked Mechanical Systems With Uncertain Parameters and Communication Delays

This paper addresses the adaptive synchronization problem of networked mechanical systems in task space with time-varying communication delays, where both kinematic and dynamic uncertainties are considered and the information flow in the networks is represented by a directed graph. Based on a novel coordination auxiliary system, we first extend existing feedback architecture to achieve synchronization of networked mechanical systems in task space with slow-varying delays. Given that abrupt turns arise for the delays sometimes, we then propose a delay-independent adaptive synchronization control scheme which removes the requirement of the slow-varying condition. Both of the two control schemes are established with time-domain approaches by using Lyapunov-Krasovskii functions. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.

Multi-Character Physical and Behavioral Interactions Controller

We extend the quadratic program (QP)-based task-space character control approach-initially intended for individual character animation-to multiple characters interacting among each other or with mobile/articulated elements of the environment. The interactions between the characters can be either physical interactions, such as contacts that can be established or broken at will between them and for which the forces are subjected to Newton's third law, or behavioral interactions, such as collision avoidance and cooperation that naturally emerge to achieve collaborative tasks from high-level specifications. We take a systematic approach integrating all the equations of motions of the characters, objects, and articulated environment parts in a single QP formulation in order to embrace and solve the most general instance of the problem, where independent individual character controllers would fail to account for the inherent coupling of their respective motions through those physical and behavioral interactions. Various types of motions/behaviors are controlled with only the one single formulation that we propose, and some examples of the original motions the framework allows are presented in the accompanying video.

A Control System for a Tool Use Robot: Drawing a Circle by Educing Functions of a Compass

[abstFig src='/00290002/12.jpg' width='260' text='Axes in the compass coordinate system' ] Robotic tool use is one of various approaches for actualizing versatility of robots, and is thus the focus of many studies. However, selection of the controllers for tool use and how to design them remains indeterminate. This paper addresses the task of drawing a circle with a compass as an example of tool use. This task mandates to deal with complex contact at multiple points and needs to educe functions of the compass to draw a circle accurately. This paper demonstrates the implementation and corresponding method of compass controller design. The method of designing the controller for the compass entails decomposing the usage of the compass into semantic units and subsequently defining a coordinate system and fabricating the controller via mapping of the semantic units to axes. The implementation of a controller for compass use indicates that the ability of the compass to accurately draw a circle is educed via mechanical constraints of the compass. We validated the implemented controller by drawing a circle and comparing the result to a circle drawn using a pencil.

Improving transparency of virtual coupling for haptic interaction with human force observer

Relying solely on virtual springs and dampers, the transparency of standard virtual coupling suffers from the device-proxy coordination error when a large interaction force is engaged (e.g., contact tasks) and also from the unmodifiable inertias of the haptic device and the virtual proxy. To overcome these limitations, we propose a novel virtual coupling scheme, which, utilizing passive decomposition and a human force observer, can maintain the device-proxy coordination error even during contact tasks, while also allowing for scaling down (or up) the apparent inertia of the coordinated device-proxy system, thereby, substantially improving transparency of the standard virtual coupling. Experiments are performed to show the performance and passivity of the proposed virtual coupling. Minimum-possible passive inertia scaling is also theoretically established via some positive-real analysis.