Full Robot Research Articles

Squat motion of a bipedal robot using real‐time kinematic prediction and whole‐body control

AbstractSquatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high‐performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole‐body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real‐time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)‐based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed‐form RKP is greatly reduced. The RKP method is verified in simulation on a heavy‐loaded bipedal robot. The robot makes rapid and large‐amplitude squatting motions, which require close‐to‐limit torque outputs. Compared with the conventional QP‐based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.

Learning Stable Vector Fields on Lie Groups

Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the performance of our proposed vector field model to learn stable vector fields for full robot poses as SE(2) and SE(3) in both simulated and real robotics tasks.

Augmented Neural Network for Full Robot Kinematic Modelling in SE(3)

Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, the inverse kinematics is directly learned, yet, learning the forward kinematics is simpler and allows computing exploiting the optimality of the controllers. Nevertheless, the learning method has no knowledge about the differential relationship between the position and velocity mappings. Currently, few works have targeted learning full robot poses considering both position and orientation. In this letter, we present a novel feedforward Artificial Neural network (ANN) architecture to learn full robot pose in SE(3) incorporating differential relationships in the learning process. Simulation and real world experiments show the capabilities of the proposed network to properly model the robot pose and its advantages over standard ANN.

Emotional musical prosody for the enhancement of trust: Audio design for robotic arm communication

Abstract As robotic arms become prevalent in industry, it is crucial to improve levels of trust from human collaborators. Low levels of trust in human–robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody (EMP) to allow the robot to respond emotionally to the user’s actions. We define EMP as musical phrases inspired by speech-based prosody used to display emotion. We tested participants’ responses to interacting with a virtual robot arm and a virtual humanoid that acted as a decision agent, helping participants select the next number in a sequence. We compared results from three versions of the application in a between-group experiment, where the robot presented different emotional reactions to the user’s input depending on whether the user agreed with the robot and whether the user’s choice was correct. One version used EMP audio phrases selected from our dataset of singer improvisations, the second version used audio consisting of a single pitch randomly assigned to each emotion, and the final version used no audio, only gestures. In each version, the robot reacted with emotional gestures. Participants completed a trust survey following the interaction, and we found that the reported trust ratings of the EMP group were significantly higher than both the single-pitch and no audio groups for the robotic arm. We found that our audio system made no significant difference in any metric when used on a humanoid robot implying audio needs to be separately designed for each platform.

Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands.

The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode.

Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell

In our study a robot was used to deliver objects for measurement into the Equator gauging system. To investigate the robot’s manipulation influence on dimensional measurements, the robot’s tasks were divided into basic functions. Based on these basic functions, nine different robot-manipulation scenarios were defined, i.e., from zero to full robot manipulation, for two measuring objects (named Magnet and PKR) and six measurement characteristics (rectangular and spherical). The robot’s manipulation influence was determined on the basis of the statistical parameters Cp, R, and the 6σ obtained from a measurement system analysis (MSA) type-1 study. The results show that the degree of implemented manipulation of the robot affects the scattering of the measurement data. However, the effect is much more pronounced in the case of length measurements than with spherical geometries. Different measuring methods (touch-triggering or scanning measurement mode, number of sampling points) were used, which showed similar measurement data. This directly indicated the influence of the robot’s manipulation on Cp, R and 6σ. Increasing the degree of the robot’s manipulation decreases the Cp value and increases the R and 6σ values for the length measurements. There is no such pronounced course in the spherical geometries, where the values of Cp, R and 6σ remain approximately the same. The main influential factor for decreasing the Cp value with increasing robot manipulation was the angular misalignment of the object’s orientation in the fixture.

COVID-19 CORONAVIRUS SCREENING ANALYSIS NEURAL NETWORK TECHNOLOGY

Low-cost, reliable and quick screening diagnosis of coronavirus can be implemented on the basis of intelligent technologies for analyzing a set of signs and symptoms with solving the problem of pattern recognition in the basis of artificial neural networks. The high degree of coronavirus infection diagnostic procedure uncertainty, the vector dimension of input factor-symptoms, fuzzy conditioning and poor formalizability of the subject condition connection with these symptoms require appropriate analytical tools. An analysis of the problem and possible solutions allows justifying the feasibilit y of implementing screening diagnostics as a solution to the problem of nonlinear optimization in a multidimensional space of high-dimensional factors and states. Artificial neural networks with compulsory training on a representative sample were chosen as a tool for implementing the project. The proposed technology brings diagnostics of coronavirus infection closer to full automation, robotization and intellectualization of complex monitoring (diagnostic) systems as the most promising technology for pattern recognition in systems with a high degree of entropy and allows you to solve the problem at the lowest cost and required performance indicators.

Learning compositional models of robot skills for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications

The rising interest in soft robotics, combined to the increasing applications in the space industry, leads to the development of novel lightweight and deployable robotic systems, that could be easily contained in a relatively small package to be deployed when required. The main challenges for soft robotic systems are the low force exertion and the control complexity. In this manuscript, a soft manipulator concept, having inflatable links, is introduced to face these issues. A prototype of the inflatable link is manufactured and statically characterized using a pseudo-rigid body model on varying inflation pressure. Moreover, the full robot model and algorithms for the load and pose estimation are presented. Finally, a control strategy, using inverse kinematics and an elastostatic approach, is developed. Experimental results provide input data for the control algorithm, and its validity domain is discussed on the basis of a simulation model. This preliminary analysis puts the basis of future advancements in building the robot prototype and developing dynamic models and robust control.

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamic environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Differential Dynamic Programming (DDP) and Alternating Direction Method of Multipliers (ADMM), suitable for solving constrained, large-scale nonlinear optimization in a distributed manner. Distinct from conventional geometric motion planners, our approach generates highly dynamic manipulation motions by incorporating the full robot and object dynamics. Furthermore, in lieu of a hierarchical planning approach, we solve a holistically integrated bilevel optimization problem involving costs from both the low-level TO and the high-level search. Simulation and experimental results demonstrate dynamic manipulation for long-horizon object sorting tasks in clutter and on a moving conveyor belt.

Multicriterial analyses of Pareto-efficiency for collaborative multi-agent systems using genetic algorithm with variations

The paper is devoted to an emerging trend in modern automation of production - collaborative multi-agent systems – characterized by the simultaneous use of the labor of people and robots in a single system in the framework of solving the target problem. The need in collaborative multi-agent systems is due to the fact that the industrial use of fully automated multicomponent systems is limited by the financial and infrastructural unavailability of various industries to switch to completely unmanned technologies. The article discusses the issue of the effectiveness of applying this approach as an alternative to full robotization of production. A multi-criteria assessment of the effectiveness of collaborative multi-agent systems as optimization knapsack problem is formulated. A multicriteria genetic algorithm based on the principle of small variations of the basic solution is presented for the solution of the stated problem. The resulting Pareto set is constructed according to the rank of possible solutions.

Geometric Gait Design for a Starfish‐Inspired Robot Using a Planar Discrete Elastic Rod Model

A starfish‐inspired robotic platform consisting of multiple soft fluidic bending actuator arms arranged with radial symmetry about a rigid hub is described. Intrinsic properties of the soft actuators are estimated via computer vision for varying input fluid pressures. The dynamic motion of individual arms and the full robot are modeled using the planar discrete elastic rod (PDER) theory. Locomotion gaits (periodic shape changes) that result in translation in the plane, separately considering fixed or rotating anchors at the end of each arm, are derived. Gait efficiency is defined as the displacement magnitude divided by a measure of the input control effort over each gait cycle, including a cost for anchor attachment. Through numerical computation, optimally efficient gaits are found and the desired motion with a pneumatic hardware prototype is demonstrated.

OpenSHC: A Versatile Multilegged Robot Controller

Multilegged robots have the ability to perform stable locomotion on relatively rough terrain. However, the complexity of legged robots over wheeled or tracked robots make them difficult to control. This paper presents OpenSHC (Open-source Syropod High-level Controller), a versatile high-level controller capable of generating gaits and poses for quasi-static multilegged robots, both simulated and with real hardware implementations. With full Robot Operating System (ROS) integration, the controller can be quickly deployed on robots with different actuators and sensor payloads. The flexibility of OpenSHC is demonstrated on the 30 degrees of freedom hexapod Bullet, analysing the energetic performance of various leg configurations, kinematic arrangements and gaits over different locomotion speeds. With OpenSHC being easily configured to different physical and locomotion specifications, a hardware-based parameter space search for optimal locomotion parameters is conducted. The experimental evaluation shows that the mammalian configuration offers lower power consumption across a range of step frequencies; with the insectoid configuration providing performance advantages at higher body velocities and increased stability at low step frequencies. OpenSHC is open-source and able to be configured for various number of joints and legs.

Passive Whole-Body Control for Quadruped Robots: Experimental Validation Over Challenging Terrain

We present experimental results using a passive whole-body control approach for quadruped robots that achieves dynamic locomotion while compliantly balancing the robot's trunk. We formulate the motion tracking as a quadratic program that takes into account the full robot rigid body dynamics, the actuation limits, the joint limits, and the contact interaction. We analyze the controller's robustness against inaccurate friction coefficient estimates and unstable footholds, as well as its capability to redistribute the load as a consequence of enforcing actuation limits. Additionally, we present practical implementation details gained from the experience with the real platform. Extensive experimental trials on the 90 kg hydraulically actuated quadruped robot validate the capabilities of this controller under various terrain conditions and gaits. The proposed approach is superior for accurate execution of highly dynamic motions with respect to the current state of the art.

Comparison of Human-Robot Interaction Torque Estimation Methods in a Wrist Rehabilitation Exoskeleton

In this paper, experimental implementation and comparative accuracy evaluation of five methods for estimation of human-robot interaction torques are presented. These methods vary from the simplest case of using solely commanded motor torques, to partial consideration of the robot dynamics, to advanced methods considering full robot dynamics such as inverse dynamics (ID) and nonlinear disturbance observer (NDO) based algorithms. Dynamic and friction models of the exoskeleton were developed and their parameters were identified using an evolutionary optimization algorithm to ensure high parameter accuracy. When used with accurate model parameters, ID method led to 20 to 22% average error, while NDO method generated 12 to 18% average error, as evaluated in experiments with a force sensor. These values compare to average error values of up to 132% for using motor torques only, and between 25 to 69% when partial dynamics were used. A sensitivity analysis of the ID and NDO methods to inaccuracies in model parameter estimations revealed considerable sensitivity of these advanced methods to model parameter variations. A summary is provided for the typical estimation accuracy levels that can be expected of these methods and discuss the limitations and considerations that should be taken into account for their use.

State observer design for a walking in-pipe robot

In this paper, a state observer design for a walking in-pipe robot is studied. The necessity of using a state observer is related to the fact that sensors have limited accuracy and are prone to producing noise. This is especially problematic for in-pipe walking robots, since they use model-based control and require accurate information of their current state. The paper shows that an iterative state observer based on solving Riccati equation provides significant improvements in the behaviour of the control system. It allows to smooth out the spikes in the control actions requested by controller and to minimize tremor of the robot links. In order to study the behaviour of the observer when different sensors are used, a performance function was introduced. It was shown that the observer allows to improve the performance of the control system for a wide range of sensor parameters. Additionally, it was shown that the introduction of the observer allows to choose higher feedback controller gains, enabling more precise control. Simulations on the full robot model, taking into account mechanical constraints and contact forces showed that the linear observer is capable of improving the behaviour of the control system of the walking robot, if measurements of the reaction forces are provided. The effects that the noise and quantization in the reaction forces measurements have on the behaviour of the state observer is studied.

Automation of inventory check in computer communication form of accounting

Methods of analysis and synthesis are used for structuring the subject matter of the research on the basis of identification and formalization of automated inventory procedures in an enterprise. A number of proposals related to installing on-site wireless scanners capable of identifying the location of inventory items are put forward. The article also gives a classification of enterprise premises by functionality, namely warehouse, trade, production, administrative, salesand other areas, which makes it possible to record relocation of inventory items.The paper deals with product labeling procedures and ways of incorporating radio identification chips into products either by manufacturers (packers) or inventory custodians following the arrival of products. The paper develops an algorithm of automated permanent inventory of assets, including a list of variable accounting information that can be recorded on RFID-chips. In addition, it becomes possible to identify employees who are responsible for storing and movement of inventory items for the purpose of compensating losses by employees involved in a shortfall.The ways to improve methods for documenting and recording inventories in accounting are described. Inventory of assets carried out with the use of radio frequency identification allows enterprises to form electronic documents automatically and record the results of accounting inventory, reduce working time, increase economic efficiency, ensure the timeliness of control and minimize theft and fraud. Further research should be undertaken to examine methods for automation of inventory control over tangible assets under conditions of full robotization of processes in an entity.

Robotics technology and agrochemical support of plant cultivation

The article provides an overview of methods for monitoring of crop condition and variability (diversity) of soil fertility in the field. Two ways of their implementation having fundamental importance to robotic technologies are marked, namely the remote and at-ground ones. Remote methods for identification of diversity of soil fertility and crop condition are infrared spectrometry of crops by means of helicopters, quadcopters, drones and other low-altitude vehicles capable to shoot the condition of targets by means of attached camera, and also the radar-photometric remote sensing which allows to mark the field areas with different agrochemicals properties in real-time mode. Among the at-ground methods of agrochemical investigation of soils and crops special attention is payed to the mesh method of soil sampling with the use of automated samplers equipped with navigational devices, the usage of relation of soil fertility with its topography and the scanning of electrical conductivity of soil. The article presents a variant of identification of field contours of soil fertility during the automatic determination of productivity of crops (principally grain-crops) in harvesting process. A block diagram of implementation of this method is given. The obtained data is used for the construction of field productivity map, which allows to divide the areas (contours) on levels of productivity. A tracked chassis with hybrid power plant, motion control equipment and machine vision device is described. The chassis serves as the practical basis for development of robotic technologies in plant cultivation, including the fertilizer distributor that will become the base robotic unit in case of full robotization of agrochemical support of plant cultivation based on unmanned mobile power units.

Optimal Motion Planning for Robotic Manipulators

This paper presents a time-optimal path planning scheme for a robot manipulator to track a specified geometric Cartesian trajectory. It considers actuator constraints, the full robot dynamics. In order to make full use of the motors capacity, the bound torques are determined by considering the bound of the generated heat. The velocity limit and the bound acceleration (deceleration) are determined by combining with dynamics. We utilize the phase plane analysis technique to generate the minimum time trajectory.

Global identification of joint drive gains and dynamic parameters of parallel robots

Off-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal—the input reference of the motor current loop—with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). Most of the papers dealing with the dynamic parameters identification of parallel robots are based on simple models, which take only the dynamics of the moving platform into account. However, for advanced applications such as output force control, in which the robot interaction force with the environment are estimated from the values of the input reference, both identifications of the full robot model and joint drive gains are required to obtain the best results. In this paper a systematic way to derive the full dynamic identification model of parallel robots is proposed in combination with a method that allows the identification of both robot inertial parameters and drive gains. The method is based on the total least squares solution of an over-determined linear system obtained with the inverse dynamic model. This model is calculated with available input reference of the motor current loop and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on a prototype of parallel robot, the Orthoglide.