Identification of Consistent Standard Dynamic Parameters of Industrial Robots

International audienceThe dynamics of each link and joint of a robot is characterized by a set of 14 standard dynamic parameters (6 for the inertia matrix, 3 for the centre of mass coordinates, 1 for the mass and 4 for the drive chain inertia and friction). It is known that only a subset of the standard parameters, called the base parameters, are identifiable using the inverse dynamic model and the linear least squares techniques. Moreover, some of the base parameters are poorly identified because they poorly affect the joint torque. Thus they can be eliminated, leading to a new subset of dynamic parameters called the essential parameters. However, the identified values of the base or the essential parameters may be physically inconsistent regarding to the loss of the positive definiteness of the robot inertia matrix. Several methods have been developed in the past to verify the physical consistency of the identified parameters but they are complicated , time consuming and lead to non-optimal parameters. To overcome these drawbacks, a new method calculates a set of optimal standard dynamic parameters which are the closest to a priori consistent dynamic parameters obtained through CAD data given by the robot manufacturers. This is a straightforward method which is based on using the SVD and the Cholesky factorization and the linear least squares techniques. The new procedure is experimentally validated on an industrial 6 degrees of freedom Stäubli TX-40 robot

Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifolds

This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.Comment: 6 pages, published in Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference o

A model-based residual approach for human-robot collaboration during manual polishing operations

A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable. Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our recently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor

Modelling and identification of a six axes industrial robot

This paper deals with the modelling and identification of a six axes industrial St ¨aubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are linear in the inertia parameters. Joint friction is described by a friction model that describes the friction behaviour in the full velocity range necessary for identification. Experimental parameter identification by means of linear least squares techniques showed to be very suited for identification of the unknown parameters, provided that the problem is properly scaled and that the influence of disturbances is sufficiently analysed and managed. An analysis of the least squares problem by means of a singular value decomposition is preferred as it not only solves the problem of rank deficiency, but it also can correctly deal with measurement noise and unmodelled dynamics

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl

Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm.Comment: 8 page

An Integrated Design and Simulation Environment for Rapid Prototyping of Laminate Robotic Mechanisms

Laminate mechanisms are a reliable concept in producing lowcost robots for educational and commercial purposes. These mechanisms are produced using low-cost manufacturing techniques which have improved significantly during recent years and are more accessible to novices and hobbyists. However, iterating through the design space to come up with the best design for a robot is still a time consuming and rather expensive task and therefore, there is still a need for model-based analysis before manufacturing. Until now, there has been no integrated design and analysis software for laminate robots. This paper addresses some of the issues surrounding laminate analysis by introducing a companion to an existing laminate design tool that automates the generation of dynamic equations and produces simulation results via rendered plots and videos. We have validated the accuracy of the software by comparing the position, velocity and acceleration of the simulated mechanisms with the measurements taken from physical laminate prototypes using a motion capture system