Compensate undesired force and torque measurements using parametric regression methods [Abstract]

Haptics as well as force and torque measurements are increasingly gaining more attention in the fields of kinesthetic learning and robot Learning from Demonstration (LfD). For such learning techniques, it is essential to obtain accurate force and torque measurements in order to enable accurate control. However, force and torque measurements using a 6-axis force and torque sensor mounted at the end-effector of an industrial robot are known to be corrupted due to the robot’s internal forces, gravity, unmodelled dynamics and nonlinear effects. Non-parametric regression is used to alleviate the negative impact of these factors on the measurements. However, non-parametric regression requires data to be available on-line which increases the system latency. In this paper, parametric regression will be used to estimate the undesired forces at the end effector for a pre-defined trajectory with limited speed. The parametric regression requires low computational complexity without intensive training over the operational space under the given assumptions. In addition, parametric regression does not need data to be available online. In this work, two compensation methods, namely linear regression and Random Forest Regression are experimentally evaluated and their relative performance is established in comparison to each other. These methods are experimentally validated using Motoman SDA10D dual-arm industrial robot controlled by Robot Operating System (ROS). The experiments showed that force and torque compensation based on linear regression and random forests has tangentially close performance

Human skill capture: A hidden Markov model of force and torque data in peg-in-a-hole assembly process

A new model has been constructed to generalise the force and torque information during a manual peg-in-a-hole (PiH) assembly process. The paper uses Hidden Markov Model analysis to interpret the state topology (transition probability) and observations (force/torque signal) in the manipulation task. The task can be recognised as several discrete states that reflect the intrinsic nature of the process. Since the whole manipulation process happens so fast, even the operator themselves cannot articulate the exact states. Those are tacit skills which are difficult to extract using human factors methodologies. In order to programme a robot to complete tasks at skill level, numerical representation of the sub-goals are necessary. Therefore, those recognised ‘hidden’ states become valuable when a detail explanation of the task is needed and when a robot controller needs to change its behaviour in different states. The Gaussian Mixture model (GMM) is used as the initial guess of observations distribution. Then a Hidden Markov Model is used to encode the state (sub-goal) topology and observation density associated with those sub-goals. The Viterbi algorithm is then applied for the model-based analysis of the force and torque signal and the classification into sub-goals. The Baum-Welch algorithm is used for training and to estimate the most likely model parameters. In addition to generic states recognition, the proposed method also enhances our understanding of the skill based performances in manual tasks

An incremental learning approach to detect muscular fatigue in human-robot collaboration

Human–robot collaboration aims to join the distinctive strengths of humans and robots to compensate for the weaknesses associated with each party and, thus, to enable synergetic effects. Robots are characteristically considered fatigue-proof. Hence, they are utilized to assist human operators during heavy pushing and pulling activities. To detect physical fatigue or high payloads held by a human operator, wearable sensors, such as electromyographys (EMGs), are deployed. The EMG data are typically processed via machine learning, which includes training models offline before an application in an online system. However, these approaches often demonstrate varying performances between offline and online applications due to subject-specific characteristics within the data. An opportunity to tackle this challenge can be found in incremental learning, as these models purely learn online and constantly fine-tune the model's structure. In this article, a Mondrian Forest is applied to predict payloads and physical fatigue of human operators during an assistance scenario with a collaborative robot. An experiment was conducted with a total of 12 participants, where the payload was increased until participants initiated an assistance request from a Universal Robots model 10 cobot. This allowed for testing whether the Mondrian Forest can accurately predict the payload and fatigue levels from the acquired EMG signals. Overall, the approach demonstrates a promising potential toward higher awareness when an operator might require assistance from a robot and ultimately toward a more effective human–robot collaboration.</p

Towards Industrial Robots as a Service (IRaaS): Flexibility, usability, safety and business models

Industrial robots form an integral part of today’s manufacturing industry, due to their high versatility, precision, and fatigue proof nature. Yet, many small and medium sized enterprises (SMEs) still predominantly rely on manual labor. The main barriers that prevent SMEs from utilizing robots to a larger degree are described to be the large initial investment, uncertainty about costs (total cost of ownership), and lack of expertise. An opportunity to eliminate these barriers can be found in servitisation. While paradigms such as software as a service (SaaS) or Robot as a Service (RaaS) already exist, these focus mostly on software (functionality) via cloud computing. In this paper, a new paradigm based on software and hardware is proposed as Industrial Robots as a Service (IRaaS), which is composed of four elements: Flexibility (Plug and Produce), Usability (Easy Programming, Intuitive Interaction), Safety (Standards, Strategies), and Business Models (Time-based, Usage-based). To provide an overview of the current state-of-the-art a scoping survey is performed on each of the four key elements from an IRaaS perspective.</p